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All Classes All Packages

A

a - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
Gets the width (x-axis) of the rectangular region.
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the size of the two-dimensional space along the x-axis, that is, the range of x, (0 < x < a).
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the size of the one-dimensional space, that is, the range of x, (0 < x < a).
a() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the size of the two-dimensional space along the x-axis, that is, the range of x, (0 < x < a).
a() - Method in class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
Get a as in the remainder (b * (x + u) + a).
a() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
Get a.
a() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
A - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
This is either [A] or [ A] [-C]
A - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
a random matrix constructed by AS159
A() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the homogeneous part, the coefficient matrix, of the linear system.
A() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Get the constraint coefficients.
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
\[ A = [A_1, A_2, ...
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets A.
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
 
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
 
A() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the coefficients, A, of the greater-than-or-equal-to constraints A * x ≥ b.
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
A() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the coefficients of the inequality constraints: A as in \(Ax \geq b\).
A() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
A() - Method in class dev.nm.stat.markovchain.SimpleMC
Gets the state transition probabilities.
A() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the estimated sequence of A.
A() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the regressor matrix.
A() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets A as in eq.
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets Ai.
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets Ai.
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets Ai.
A(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
Compute an.
A_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Combine all A data as a matrix.
A_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
A_l_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Combine all linear blocks as a matrix.
A_q(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets \({A^q}_i\).
A_q_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
A^q = [{A^q}_1, {A^q}_2, ...
A_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
a0() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Gets the constant coefficient.
a0() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the constant term.
a1() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
Gets the ARCH coefficient.
AbelianGroup<G> - Interface in dev.nm.algebra.structure
An Abelian group is a group with a binary additive operation (+), satisfying the group axioms: closure associativity existence of additive identity existence of additive opposite commutativity of addition
ABMPredictorCorrector - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector pair.
ABMPredictorCorrector1 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1.
ABMPredictorCorrector1() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
 
ABMPredictorCorrector2 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2.
ABMPredictorCorrector2() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
 
ABMPredictorCorrector3 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3.
ABMPredictorCorrector3() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
 
ABMPredictorCorrector4 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4.
ABMPredictorCorrector4() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
 
ABMPredictorCorrector5 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5.
ABMPredictorCorrector5() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
 
abs(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the absolute values.
abs(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the absolute values of a vector, element-by-element.
ABSOLUTE - tech.nmfin.returns.ReturnsCalculators
The return is defined as the difference between the values of the portfolio.
ABSOLUTE_ZERO_T0 - Static variable in class dev.nm.misc.PhysicalConstants
The absolute zero temperature in Celsius (°C).
absoluteError(double, double) - Static method in class dev.nm.number.DoubleUtils
Compute the absolute difference between x1 and x0.
AbsoluteErrorPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
This penalty function sums up the absolute error penalties.
AbsoluteErrorPenalty(EqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
Construct an absolute error penalty function from a collection of equality constraints.
AbsoluteErrorPenalty(EqualityConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
Construct an absolute error penalty function from a collection of equality constraints.
AbsoluteErrorPenalty(EqualityConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
Construct an absolute error penalty function from a collection of equality constraints.
AbsoluteTolerance - Class in dev.nm.misc.algorithm.iterative.tolerance
The stopping criteria is that the norm of the residual r is equal to or smaller than the specified tolerance, that is, ||r||2 ≤ tolerance
AbsoluteTolerance() - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
Construct an instance with AbsoluteTolerance.DEFAULT_TOLERANCE.
AbsoluteTolerance(double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
Construct an instance with specified tolerance.
AbstractBivariateEVD - Class in dev.nm.stat.evt.evd.bivariate
 
AbstractBivariateEVD() - Constructor for class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
AbstractBivariateProbabilityDistribution - Class in dev.nm.stat.distribution.multivariate
 
AbstractBivariateProbabilityDistribution() - Constructor for class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
 
AbstractBivariateRealFunction - Class in dev.nm.analysis.function.rn2r1
A bivariate real function takes two real arguments and outputs one real value.
AbstractBivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
 
AbstractHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation.
AbstractHybridMCMC(Vector, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
Constructs a new instance with the given parameters.
AbstractMetropolis - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
The Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function f proportional to the PDF from which we wish to sample.
AbstractMetropolis(Vector, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
Constructs a new instance with the given parameters.
AbstractR1RnFunction - Class in dev.nm.analysis.function.rn2rm
This is a function that takes one real argument and outputs one vector value.
AbstractR1RnFunction(int) - Constructor for class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
 
AbstractRealScalarFunction - Class in dev.nm.analysis.function.rn2r1
This abstract implementation implements Function.dimensionOfRange() by always returning 1, and Function.dimensionOfDomain() by returning the input argument for the dimension of domain.
AbstractRealScalarFunction(int) - Constructor for class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
Construct an instance with the dimension of the domain.
AbstractRealVectorFunction - Class in dev.nm.analysis.function.rn2rm
This abstract implementation implements Function.dimensionOfDomain() and Function.dimensionOfRange() by returning the input arguments at constructor.
AbstractRealVectorFunction(int, int) - Constructor for class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
 
AbstractTrivariateRealFunction - Class in dev.nm.analysis.function.rn2r1
A trivariate real function takes three real arguments and outputs one real value.
AbstractTrivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
 
AbstractUnivariateRealFunction - Class in dev.nm.analysis.function.rn2r1.univariate
A univariate real function takes one real argument and outputs one real value.
AbstractUnivariateRealFunction() - Constructor for class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
 
acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
 
acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
 
acceptanceProbability(Vector, double, Vector, double, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
 
acceptanceProbability(Vector, double, Vector, double, double) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.TemperedAcceptanceProbabilityFunction
Computes the probability that the next state transition will be accepted.
acceptanceRate() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
Gets the acceptance rate, i.e.
acceptanceTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
 
acceptanceTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
 
acceptanceTemperature(int) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
Gets the acceptance temperature \(T^A_t\) at time t.
ACERAnalysis - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima \(M\) distribution from observations.
ACERAnalysis() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
Create an instance with the default values.
ACERAnalysis(int, int, double, boolean, boolean) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
Create an instance with various options listed below.
ACERAnalysis.Result - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
 
ACERByCounting - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
Estimate epsilons by counting conditional exceedances from the observations.
ACERByCounting(double[], int) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
Create an instance for estimating epsilon for each of the given barrier levels.
ACERConfidenceInterval - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Using the given (estimated) ACER function as the mean, find the ACER parameters at the lower and upper bounds of the estimated confidence interval of ACER values.
ACERConfidenceInterval(ACERFunction.ACERParameter, EmpiricalACER, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
ACERFunction - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
The ACER (Average Conditional Exceedance Rate) function \(\epsilon_k(\eta)\) approximates the probability \[ \epsilon_k(\eta) = Pr(X_k > \eta | X_1 \le \eta, X_2 \le \eta, ..., X_{k-1} \le \eta) \] for a sequence of stochastic process observations \(X_i\) with a k-step memory.
ACERFunction(ACERFunction.ACERParameter) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction
 
ACERFunction.ACERParameter - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Parameters for ACERFunction.
ACERInverseFunction - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
The inverse of the ACER function.
ACERInverseFunction(ACERFunction.ACERParameter) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERInverseFunction
 
ACERLogFunction - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
The ACER function in log scale (base e), i.e., \(log(\epsilon_k(\eta))\).
ACERLogFunction(ACERFunction.ACERParameter) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERLogFunction
Create an instance with the ACER function parameter.
ACERParameter(double[]) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
Create an instance with a double[] which contains q, b, a, c.
ACERParameter(double, double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
ACERReturnLevel - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Given an ACER function, compute the return level \(\eta\) for a given return period \(R\).
ACERReturnLevel(ACERFunction.ACERParameter, double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERReturnLevel
Create an instance with the (estimated) ACER function parameter and the total number of events.
ACERUtils - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
Utility functions used in ACER empirical analysis.
ACERUtils() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils
 
acos(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Inverse of cosine.
acosh(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic cosine of a value; the returned hyperbolic angle is positive.
acot(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc cotangent of a value; the returned angle is in the range -pi/2 through pi/2.
acot2(double, double) - Static method in class dev.nm.geometry.TrigMath
Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta).
acoth(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic cotangent of a value.
acovers(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc coversine of a value; the returned angle is in the range -pi/2 through pi/2.
acsc(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc cosecant of a value; the returned angle is in the range -pi/2 through pi/2.
acsch(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic cosecant of a value.
actions() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the the sequence of actions taken.
ActiveList - Interface in dev.nm.misc.algorithm.bb
This interface defines the node popping strategy used in a branch-and-bound algorithm, e.g., depth-first-search, best-first-search.
ActiveSet - Class in dev.nm.misc.algorithm
This class keeps track of the active and inactive indices.
ActiveSet(boolean) - Constructor for class dev.nm.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
ActiveSet(boolean, int[]) - Constructor for class dev.nm.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
ActiveSet(boolean, Collection<Integer>) - Constructor for class dev.nm.misc.algorithm.ActiveSet
Construct a working set of active/inactive indices.
activeSize() - Method in class dev.nm.misc.algorithm.ActiveSet
Get the number of active indices.
AdamsBashforthMoulton - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
This class uses an Adams-Bashford predictor and an Adams-Moulton corrector of the specified order.
AdamsBashforthMoulton(ABMPredictorCorrector, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
Create a new instance of the Adams-Bashforth-Moulton method using the given predictor-corrector pair.
AdamsBashforthMoulton(ABMPredictorCorrector, double, ODEIntegrator) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
Create a new instance of the Adams-Bashforth-Moulton method using the given predictor-corrector pair and the given ODE integrator.
add(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
add(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
add(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
add(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
add(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Add a constant to all entries in this vector.
add(double) - Method in class dev.nm.combinatorics.Counter
Add a number to the counter.
add(double...) - Method in class dev.nm.combinatorics.Counter
Add numbers to the counter.
add(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Add a double value to each element in an array.
add(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
 
add(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
Add two double arrays, entry-by-entry.
add(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
add(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
add(double, T) - Method in class dev.nm.misc.algorithm.Bins
Add a valued item to the bin.
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
add(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
this + that
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Computes the sum of two diagonal matrices.
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
add(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
add(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
add(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
A1 + A2
add(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
add(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Add up the elements in this and that, element-by-element.
add(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
add(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
add(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
add(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
add(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
add(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
add(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
add(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
\(this + that\)
add(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Adds a constant to a vector, element-by-element.
add(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Adds two vectors, element-by-element.
add(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
add(OrderedPairs) - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Dynamically add points to the step function.
add(Interval<T>) - Method in class dev.nm.interval.Intervals
Add an interval to the set.
add(Interval<T>...) - Method in class dev.nm.interval.Intervals
Add intervals to the set.
add(BBNode) - Method in interface dev.nm.misc.algorithm.bb.ActiveList
Add a node to the active list.
add(Complex) - Method in class dev.nm.number.complex.Complex
 
add(Real) - Method in class dev.nm.number.Real
 
add(SOCPGeneralConstraint) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
Add an SOCP constraint.
add(SOCPLinearEquality) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
 
add(SOCPLinearInequality) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
 
add(G) - Method in interface dev.nm.algebra.structure.AbelianGroup
+ : G × G → G
add(T) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
addActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Add an active constraint by index.
addActive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
Add active indices.
addActive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
Add active indices.
addAll(Collection<? extends T>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
addCheck(PairingCheck) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
addColAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Adds a column at i.
addColAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Adds a column at i.
addColumn(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Column addition: A[, j1] = A[, j1] + c * A[, j2]
addData(double...) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
Update the statistic with more data.
addData(double...) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
Update the statistic with more data.
addData(double...) - Method in class dev.nm.stat.descriptive.covariance.Covariance
Update the covariance statistic with more data.
addData(double...) - Method in class dev.nm.stat.descriptive.moment.Kurtosis
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.Mean
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.Moments
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.Skewness
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.Variance
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
 
addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
addData(double...) - Method in class dev.nm.stat.descriptive.rank.Max
 
addData(double...) - Method in class dev.nm.stat.descriptive.rank.Min
 
addData(double...) - Method in class dev.nm.stat.descriptive.rank.Quantile
 
addData(double...) - Method in interface dev.nm.stat.descriptive.Statistic
Recompute the statistic with more data, incrementally if possible.
addData(double...) - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
 
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
Add the given two samples.
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
Add the given two samples.
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.covariance.Covariance
Update the covariance statistic with more data.
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
addData(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
 
addData(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
 
addData(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.interpolation.OnlineInterpolator
Add more points for interpolation.
addEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
 
addEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree
Add an edge to the tree, connecting v1, the parent and v2..., the children.
addEdge(E) - Method in interface dev.nm.graph.Graph
Adds an edge to this graph.
addEdge(E) - Method in class dev.nm.graph.type.SparseDAGraph
 
addEdge(E) - Method in class dev.nm.graph.type.SparseGraph
 
addEdges(Graph<V, E>, E...) - Static method in class dev.nm.graph.GraphUtils
Add a set of edges to a graph.
addFactor(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Adds the indexed factor.
addInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Add an inactive constraint by index.
addInactive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet
Add inactive indices.
addInactive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet
Add inactive indices.
addIterate(Vector) - Method in class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
 
addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
 
addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
 
addIterate(S) - Method in interface dev.nm.misc.algorithm.iterative.monitor.IterationMonitor
Record a new iteration state.
addIterate(S) - Method in class dev.nm.misc.algorithm.iterative.monitor.NullMonitor
 
AdditiveModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess
The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
AdditiveModel(double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.AdditiveModel
Construct a univariate time series by adding up the components.
AdditiveModel(double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.AdditiveModel
Construct a univariate time series by adding up the components.
addRow(double, double[]) - Method in class dev.nm.misc.datastructure.MathTable
Adds a row to the table.
addRow(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Row addition: A[i1, ] = A[i1, ] + c * A[i2, ]
addRow(PanelData.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Inserts a row of data into the panel.
addRow(S, T, double...) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Inserts a row of data into the panel.
addRowAt(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Adds a row at i.
addRowAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Adds a row at i.
addRows(double[][]) - Method in class dev.nm.misc.datastructure.MathTable
Adds rows by a double[][].
addVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
addVertex(V) - Method in interface dev.nm.graph.Graph
Adds a vertex to this graph.
addVertex(V) - Method in class dev.nm.graph.type.SparseGraph
 
addVertex(V) - Method in class dev.nm.graph.type.SparseTree
 
addVertices(Graph<V, ?>, V...) - Static method in class dev.nm.graph.GraphUtils
Add a set of vertices to a graph.
ADFAsymptoticDistribution - Class in dev.nm.stat.test.timeseries.adf
This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test statistic.
ADFAsymptoticDistribution(TrendType) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
ADFAsymptoticDistribution(TrendType, int, int, long) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
ADFAsymptoticDistribution1 - Class in dev.nm.stat.test.timeseries.adf
Deprecated.
ADFAsymptoticDistribution1(int, int, ADFAsymptoticDistribution1.Type, long) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
ADFAsymptoticDistribution1(ADFAsymptoticDistribution1.Type) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1
Deprecated.
Construct an asymptotic distribution for the augmented Dickey-Fuller test statistic.
ADFAsymptoticDistribution1.Type - Enum in dev.nm.stat.test.timeseries.adf
Deprecated.
the available types of Dickey-Fuller tests
ADFDistribution - Class in dev.nm.stat.test.timeseries.adf
This represents an Augmented Dickey Fuller distribution.
ADFDistributionTable - Class in dev.nm.stat.test.timeseries.adf.table
A table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters.
ADFDistributionTable(MathTable) - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable
 
ADFDistributionTable_CONSTANT_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the JohansenAsymptoticDistribution.TrendType.CONSTANT case.
ADFDistributionTable_CONSTANT_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_lag0
 
ADFDistributionTable_CONSTANT_TIME_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the JohansenAsymptoticDistribution.TrendType.CONSTANT_TIME case.
ADFDistributionTable_CONSTANT_TIME_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_CONSTANT_TIME_lag0
 
ADFDistributionTable_NO_CONSTANT_lag0 - Class in dev.nm.stat.test.timeseries.adf.table
This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the JohansenAsymptoticDistribution.TrendType.NO_CONSTANT case.
ADFDistributionTable_NO_CONSTANT_lag0() - Constructor for class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable_NO_CONSTANT_lag0
 
ADFFiniteSampleDistribution - Class in dev.nm.stat.test.timeseries.adf
This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test statistics.
ADFFiniteSampleDistribution(int) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
ADFFiniteSampleDistribution(int, TrendType) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
Construct a finite sample distribution for the original Dickey-Fuller test statistic.
ADFFiniteSampleDistribution(int, TrendType, boolean, int) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
ADFFiniteSampleDistribution(int, TrendType, boolean, int, int, int, long) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution
Construct a finite sample distribution for the Augmented Dickey-Fuller test statistic.
Aeq() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the coefficients, Aeq, of the equality constraints Aeq * x ≥ beq.
Aeq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
Aeq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
Aeq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the coefficients of the equality constraints: Aeq as in \(A_{eq}x = b_{eq}\).
Aeq() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
aexsec(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc exsecant of a value; the returned angle is in the range 0.0 through pi.
AFTER - dev.nm.interval.IntervalRelation
X takes place after Y.
AfterIterations - Class in dev.nm.misc.algorithm.stopcondition
Stops after a given number of iterations.
AfterIterations(int) - Constructor for class dev.nm.misc.algorithm.stopcondition.AfterIterations
Stops after a given number of iterations.
AfterNoImprovement - Class in dev.nm.misc.algorithm.stopcondition
 
AfterNoImprovement(int) - Constructor for class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
 
AhatEstimation - Class in tech.nmfin.meanreversion.daspremont2008
Estimates the coefficient of a VAR(1) model by penalized maximum likelihood.
AhatEstimation(Matrix, Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
Estimates the coefficient matrix of a vector autoregressive process of order 1.
ahav(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc haversine of a value; the returned angle is in the range 0 to pi.
AIC() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Gets the Akaike information criterion (AIC).
AIC() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
Gets the AIC.
AIC() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
Gets the Akaike information criterion.
AIC() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Compute the AIC of fitted model.
AIC() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Compute the AIC, a model selection criterion.
AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
AIC(Vector, Vector, Vector, double, double, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
AIC = 2 * #param - 2 * log-likelihood
AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
AIC(Vector, Vector, Vector, double, double, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
AICC() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Compute the AICC of fitted model.
AICC() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Compute the AICC, a model selection criterion.
ak - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
the increment in the search direction
algebraicMultiplicity() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get the multiplicity of the eigenvalue (a root) of the characteristic polynomial.
allForecasts() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets all the predictions of the next h steps in one vector.
allForecasts() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets all the predictions of the next h steps in one vector.
AllIntegers() - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
 
allMSEs() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
allMSEs() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets all the mean squared errors (MSE) of the h-step ahead predictions.
alpha - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
α: the shape parameter
alpha() - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the set of adjusting coefficients, by columns.
alpha() - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel
Gets the individual/subject specific terms.
alpha() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Gets the ARCH coefficient.
alpha() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the ARCH coefficients.
alpha(double) - Method in class dev.nm.stat.test.distribution.AndersonDarlingPValue
Gets the p-value for a test statistic.
alpha(Vector, Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
Get the percentage increment along the minimizer increment direction.
alpha(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
alpha(Vector, Vector, Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
Get the percentage increment along the minimizer increment direction.
alpha(Vector, Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
Get the percentage increment along the minimizer increment direction.
alpha0 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
alpha1 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
AlternatingDirectionImplicitMethod - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
Alternating direction implicit (ADI) method is an implicit method for obtaining numerical approximations to the solution of a HeatEquation2D.
AlternatingDirectionImplicitMethod(double) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
Create an ADI method with the given precision parameter.
AlternatingDirectionImplicitMethod(double, boolean) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
Create an ADI method with the given precision parameter, and choice for using multi-core parallel computation for higher performance.
ANALYTIC - dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
The line search is done analytically.
ANALYTICAL - dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
use the analytical gradient formulae in the references, eqs.
AndersonDarling - Class in dev.nm.stat.test.distribution
This algorithm calculates the Anderson-Darling k-sample test statistics and p-values.
AndersonDarling(double[]...) - Constructor for class dev.nm.stat.test.distribution.AndersonDarling
Runs the Anderson-Darling test.
AndersonDarlingPValue - Class in dev.nm.stat.test.distribution
This algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given.
AndersonDarlingPValue(int) - Constructor for class dev.nm.stat.test.distribution.AndersonDarlingPValue
Construct the Anderson-Darling distribution for a particular number of samples.
AndStopConditions - Class in dev.nm.misc.algorithm.stopcondition
Combines an arbitrary number of stop conditions, terminating when all conditions are met.
AndStopConditions(StopCondition...) - Constructor for class dev.nm.misc.algorithm.stopcondition.AndStopConditions
 
angle(double, double, double) - Static method in class dev.nm.geometry.TrigMath
Returns the angle \(\alpha\) opposite the side a, given the three side-lengths of the triangle.
angle(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
angle(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
angle(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
angle(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Measure the angle, \(\theta\), between this and that.
angle(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the angle between two vectors.
angle(Pair, Pair, Pair) - Static method in class dev.nm.geometry.TrigMath
Given a the coordinates of A, B and C, the apices of triangle ABC, returns the value of the angle \(alpha\) at apex A.
angle(H) - Method in interface dev.nm.algebra.structure.HilbertSpace
∠ : H × H → F
AnnealingFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
An annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature.
AntitheticVariates - Class in dev.nm.stat.random.variancereduction
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path - that is given a path \(\varepsilon_1,\dots,\varepsilon_M\) to also take, for example, \(-\varepsilon_1,\dots,-\varepsilon_M\) or \(1-\varepsilon_1,\dots,1-\varepsilon_M\).
AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates
Estimates \(E(f(X_1))\) and use AntitheticVariates.INVERSE as the default antithetic path.
AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator, UnivariateRealFunction) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates
Estimates \(E(f(X_1))\), where f is a function of a random variable.
AntoniouLu2007 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This implementation is based on Algorithm 14.5 in the reference.
APERY - Static variable in class dev.nm.misc.Constants
the Apery's constant
APPROXIMATELY_MEDIAN_UNBIASED - dev.nm.stat.descriptive.rank.Quantile.QuantileType
default: the resulting quantile estimates are approximately median-unbiased regardless of the distribution of the sample
APPROXIMATELY_UNBIASED_IF_DATA_IS_NORMAL - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the resulting quantile estimates are approximately unbiased for the expected order statistics if the sample is normally distributed
AR() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
Get the AR coefficients.
AR(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the i-th AR coefficient; AR(0) = 1.
AR(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the i-th AR coefficient; AR(0) = 1.
AR1GARCH11Model - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
An AR1-GARCH11 model takes this form.
AR1GARCH11Model(double, double, double, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
 
AR2() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: adjusted R-squared
Arc<V> - Interface in dev.nm.graph
An arc is an ordered pair of vertices.
areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.
areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Checks whether the constraints are satisfied with a solution vector x.
areAllConstraintsSatisfied(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Checks whether the constraints are satisfied with a solution vector x.
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
 
areAllConstraintsSatisfied(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
 
areAllSparse(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if all matrices are SparseMatrix.
areAllSparse(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if all vectors are SparseVector.
areEqual(Matrix, Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks the equality of two matrices up to a precision.
areEqual(Vector, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are equal, i.e., v1 - v2 is a zero vector, up to a precision.
areOrthogonal(Vector[], double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a set of vectors are orthogonal, i.e., for any v1, v2 in v, v1 ∙ v2 == 0.
areOrthogonal(Vector, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are orthogonal, i.e., v1 ∙ v2 == 0.
areOrthogonormal(Vector[], double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a set of vectors are orthogonormal.
areOrthogonormal(Vector, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if two vectors are orthogonormal.
arg() - Method in class dev.nm.number.complex.Complex
Get the θ of the complex number in polar representation.
ArgumentAssertion - Class in dev.nm.misc
Utility class for checking numerical arguments.
ARIMAForecast - Class in dev.nm.stat.timeseries.linear.univariate.arima
Forecasts an ARIMA time series using the innovative algorithm.
ARIMAForecast(IntTimeTimeSeries, int, int, int, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
Constructs a forecaster for a time series assuming ARIMA model.
ARIMAForecast(IntTimeTimeSeries, ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
Constructs a forecaster for a time series assuming ARIMA model.
ARIMAForecast.Forecast - Class in dev.nm.stat.timeseries.linear.univariate.arima
The forecast value and variance.
ARIMAForecastMultiStep - Class in dev.nm.stat.timeseries.linear.univariate.arima
Makes forecasts for a time series assuming an ARIMA model using the innovative algorithm.
ARIMAForecastMultiStep(IntTimeTimeSeries, ARIMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Makes the h-step ahead prediction for an ARIMA model.
ARIMAModel - Class in dev.nm.stat.timeseries.linear.univariate.arima
An ARIMA(p, d, q) process, Xt, is such that \[ (1 - B)^d X_t = Y_t \] where B is the backward or lag operator, d the order of difference, Yt an ARMA(p, q) process, for which \[ Y_t = \mu + \Sigma \phi_i Y_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \]
ARIMAModel(double[], int, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with unit variance and zero-intercept (mu).
ARIMAModel(double[], int, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with zero-intercept (mu).
ARIMAModel(double, double[], int, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model with unit variance.
ARIMAModel(double, double[], int, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Construct a univariate ARIMA model.
ARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Copy constructor.
ARIMASim - Class in dev.nm.stat.timeseries.linear.univariate.arima
This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process.
ARIMASim(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model, using random standard Gaussian innovations.
ARIMASim(ARIMAModel, double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model.
ARIMASim(ARIMAModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
Construct an ARIMA model.
ARIMAXModel - Class in dev.nm.stat.timeseries.linear.univariate.arima
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.
ARIMAXModel(double[], int, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with unit variance and zero-intercept (mu).
ARIMAXModel(double[], int, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with zero-intercept (mu).
ARIMAXModel(double, double[], int, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model with unit variance.
ARIMAXModel(double, double[], int, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Construct a univariate ARIMAX model.
ARIMAXModel(ARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Copy constructor.
ARMAFit - Interface in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
This interface represents a fitting method for estimating φ, θ, μ, σ2 in an ARMA model.
ARMAForecast - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Forecasts an ARMA time series using the innovative algorithm.
ARMAForecast(IntTimeTimeSeries, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Constructs a forecaster for a time series assuming ARMA model.
ARMAForecast(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Constructs a forecaster for a time series assuming ARMA model.
ARMAForecastMultiStep - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm.
ARMAForecastMultiStep(double[], ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastMultiStep(IntTimeTimeSeries, ARMAModel, int, InnovationsAlgorithm, ARMAForecastOneStep) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Makes the h-step ahead prediction for an ARMA model.
ARMAForecastOneStep - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
ARMAForecastOneStep(double[], ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAForecastOneStep(IntTimeTimeSeries, ARMAModel, InnovationsAlgorithm) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Makes the one-step ahead prediction for an ARMA model.
ARMAGARCHFit - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
This implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood Estimation.
ARMAGARCHFit(double[], int, int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Constructs a model with the default tolerance and maximum number of iterations.
ARMAGARCHFit(double[], int, int, int, int, double, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Constructs a model.
ARMAGARCHModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
An ARMA-GARCH model takes this form: \[ X_t = \mu + \sum_{i=1}^p \phi_i X_{t-i} + \sum_{i=1}^q \theta_j \epsilon_{t-j} + \epsilon_t, \\ \epsilon_t = \sqrt{h_t\eta_t}, \\ h_t = \alpha_0 + \sum_{i=1}^{r} (\alpha_i e_{t-i}^2) + \sum_{i=1}^{s} (\beta_i h_{t-i}) \]
ARMAGARCHModel(ARMAModel, GARCHModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
Construct a univariate ARMA-GARCH model.
ARMAModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
A univariate ARMA model, Xt, takes this form.
ARMAModel(double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with unit variance and zero-intercept (mu).
ARMAModel(double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with zero-intercept (mu).
ARMAModel(double, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model with unit variance.
ARMAModel(double, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Construct a univariate ARMA model.
ARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Copy constructor.
armaxMean(double[], double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Compute the univariate ARMAX conditional mean.
armaxMean(Matrix, Matrix, Vector) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Compute the multivariate ARMAX conditional mean.
ARMAXModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
ARMAXModel(double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with unit variance and zero-intercept (mu).
ARMAXModel(double[], double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with zero-intercept (mu).
ARMAXModel(double, double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model with unit variance.
ARMAXModel(double, double[], double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Construct a univariate ARMAX model.
ARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel
Copy constructor.
ARModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
This class represents an AR model.
ARModel(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with unit variance and zero-intercept (mu).
ARModel(double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with zero-intercept (mu).
ARModel(double, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model with unit variance.
ARModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Construct a univariate AR model.
ARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel
Copy constructor.
ArrayUtils - Class in dev.nm.misc
 
ARResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
 
ARResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
 
ARResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
 
ARTIFICIAL - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the artificial variable, x0, pp.
ARTIFICIAL - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
ARTIFICIAL_COST - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the artificial objective, z0, pp.
ARTIFICIAL_COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
AS_26 - dev.nm.stat.descriptive.rank.Rank.TiesMethod
Algorithm AS 26.
AS159 - Class in dev.nm.stat.test.distribution.pearson
Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums.
AS159(int[], int[]) - Constructor for class dev.nm.stat.test.distribution.pearson.AS159
Constructs a random table generator according to the row and column totals.
AS159(int[], int[], RandomLongGenerator) - Constructor for class dev.nm.stat.test.distribution.pearson.AS159
Constructs a random table generator according to the row and column totals.
AS159.RandomMatrix - Class in dev.nm.stat.test.distribution.pearson
a random matrix generated by AS159 and its probability
asArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Cast this data structure as a double[].
asec(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc secant of a value; the returned angle is in the range 0.0 through pi.
asech(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic secant of a value.
asin(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Inverse of sine.
asinh(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic sine of a value.
assertEqual(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x equal to bound.
assertEqual(T, T, String, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if two Numbers x1 and x2 are equal.
assertFalse(boolean, String, Object...) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument condition is false.
assertGreaterThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is greater than bound.
assertLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is less than bound.
assertNegative(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is negative.
assertNonNegative(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is non-negative.
assertNonPositive(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is non-positive.
assertNormalDouble(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is a normal double value (that is, NOT Double.NaN nor infinity).
assertNormalFloat(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is a normal float value (that is, NOT Float.NaN nor infinity).
assertNotGreaterThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is not greater than bound.
assertNotInfinity(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is NOT a Double.POSITIVE_INFINITY nor Double.NEGATIVE_INFINITY.
assertNotInfinity(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is NOT a Float.POSITIVE_INFINITY nor Float.NEGATIVE_INFINITY.
assertNotLessThan(T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is not less than bound.
assertNotNaN(double, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is NOT a Double.NaN.
assertNotNaN(float, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument is NOT a Float.NaN.
assertNotNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if obj is not null.
assertNull(Object, String) - Static method in class dev.nm.misc.ArgumentAssertion
Check if obj is null.
assertPositive(T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test if Number x is positive.
assertRange(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test whether the specified Number occurs within the range [low, high] (both inclusive).
assertRangeLeftOpen(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test whether the specified Number occurs within the range (low, high] (left exclusive, right inclusive).
assertRangeOpen(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test whether the specified Number occurs within the range (low, high) (both exclusive).
assertRangeRightOpen(T, T, T, String) - Static method in class dev.nm.misc.ArgumentAssertion
Test whether the specified Number occurs within the range [low, high) (left inclusive, right exclusive).
assertTrue(boolean, String, Object...) - Static method in class dev.nm.misc.ArgumentAssertion
Check if an argument condition is true.
ASYMPTOTIC - dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
The default is the asymptotic distribution of Fisher's exact test.
asymptoticCDF(double) - Static method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
This is the asymptotic distribution of the Kolmogorov distribution.
asymptoticCDF(double, double) - Static method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
This is the asymptotic distribution of the one-sided Kolmogorov distribution.
atan(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Inverse of tangent.
atanh(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc hyperbolic tangent of a value.
ATOMIC_MASS_MU - Static variable in class dev.nm.misc.PhysicalConstants
The atomic mass constant \(m_u\) in kilograms (kg).
AtThreshold - Class in dev.nm.misc.algorithm.stopcondition
Stops when the value reaches a given value with a given precision.
AtThreshold(double, double) - Constructor for class dev.nm.misc.algorithm.stopcondition.AtThreshold
Stops when the value reaches a given value with a given precision.
AUGMENTED_DICKEY_FULLER - dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
Deprecated.
the augmented version of the Dickey-Fuller test
AugmentedDickeyFuller - Class in dev.nm.stat.test.timeseries.adf
The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time series stationary.
AugmentedDickeyFuller(double[]) - Constructor for class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
AugmentedDickeyFuller(double[], TrendType, int, ADFDistribution) - Constructor for class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
Performs the Augmented Dickey-Fuller test to test for the existence of unit root.
AutoARIMAFit - Class in dev.nm.stat.timeseries.linear.univariate.arima
Selects the order and estimates the coefficients of an ARIMA model automatically by AIC or AICC.
AutoARIMAFit(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
Automatically selects and estimates the ARIMA model using default parameters.
AutoARIMAFit(double[], int, int, int, int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
Automatically selects and estimates the ARIMA model using custom parameters.
AutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
AutoCorrelation(ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
Compute the auto-correlation function for an ARMA model.
AutoCorrelationFunction - Class in dev.nm.stat.timeseries.linear.univariate
This is the auto-correlation function of a univariate time series {xt}.
AutoCorrelationFunction() - Constructor for class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
 
AutoCovariance - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
AutoCovariance(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
Computes the auto-covariance function for an ARMA model.
AutoCovarianceFunction - Class in dev.nm.stat.timeseries.linear.univariate
This is the auto-covariance function of a univariate time series {xt}.
AutoCovarianceFunction() - Constructor for class dev.nm.stat.timeseries.linear.univariate.AutoCovarianceFunction
 
autoEpsilon(double...) - Static method in class dev.nm.misc.PrecisionUtils
Guess a reasonable precision parameter.
autoEpsilon(double[]...) - Static method in class dev.nm.misc.PrecisionUtils
Guess a reasonable precision parameter.
autoEpsilon(MatrixTable) - Static method in class dev.nm.misc.PrecisionUtils
Guess a reasonable precision parameter.
AutoParallelMatrixMathOperation - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
This class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation.
AutoParallelMatrixMathOperation() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
AutoParallelMatrixMathOperation(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
AVERAGE - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
AverageImplicitModelPCA - Class in dev.nm.stat.factor.implicitmodelpca
This decomposes the observations into a model of one explicit factor, the average observation per subject, and implicit factors.
AverageImplicitModelPCA(Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA
Constructs an explicit-implicit model for a time series of vectored observations
AverageImplicitModelPCA(Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA
Constructs an explicit-implicit model for a time series of vectored observations
avers(double) - Static method in class dev.nm.geometry.TrigMath
Returns the arc versine of a value; the returned angle is in the range zero through pi.
avg_duration1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
avg_duration2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
AVOGADRO_NA - Static variable in class dev.nm.misc.PhysicalConstants
The Avogadro constant \(N_A\), \(L\) in units per mole (mol-1).

B

b() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the non-homogeneous part, the right-hand side vector, of the linear system.
b() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
Gets the height (y-axis) of the rectangular region.
b() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the size of the two-dimensional space along the y-axis, that is, the range of y, (0 < y < b).
b() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the size of the two-dimensional space along the y-axis, that is, the range of y, (0 < y < b).
b() - Method in class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
Get b as in the remainder (b * (x + u) + a).
b() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
Get b.
b() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Get the constraint values.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets b.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Gets the objective vector, b, in the compact form.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets b.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets b.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets b.
b() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the values, b, of the greater-than-or-equal-to constraints A * x ≥ b.
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
b() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the values of the inequality constraints: b as in \(Ax \geq b\).
b() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
b() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
b() - Method in interface dev.nm.stat.random.variancereduction.ControlVariates.Estimator
Gets the optimal b.
b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets the implicit factor loading for the n-th subject.
b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets the factor loading for the n-th subject.
B - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the constraint values (the last column)
B - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
B() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
B() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
 
B() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Gets B, which is the square upper part of U.t().multiply(A).multiply(V).
B() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
Get the double precision matrix.
B() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets B, the implicit factor loading matrix.
B() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets B, the factor loading matrix.
B() - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
Gets the conditional probabilities of the observation symbols: rows correspond to state; columns corresponds symbols.
B() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets B as in eq.
B() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
 
B(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the Brownian motion value at the i-th time point.
B(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials
Compute bn.
b1() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
Gets the GARCH coefficient.
backSearch(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
Finds H22 such that H22 is the largest unreduced Hessenberg sub-matrix, and H33 is upper quasi-triangular.
backSearch(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
 
backSearch(Matrix, int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
 
backward(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
Perform a backward sweep.
BACKWARD - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
backward difference
BackwardElimination - Class in dev.nm.stat.regression.linear.glm.modelselection
Constructs a GLM model for a set of observations using the backward elimination method.
BackwardElimination(GLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination
Constructs a GLM model using the backward elimination method, with EliminationByAIC as the default algorithm.
BackwardElimination(GLMProblem, BackwardElimination.Step) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination
Constructs a GLM model using the backward elimination method.
BackwardElimination.Step - Interface in dev.nm.stat.regression.linear.glm.modelselection
 
BackwardSubstitution - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
Backward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U.
BackwardSubstitution() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
 
BanachSpace<B,​F extends Field<F> & Comparable<F>> - Interface in dev.nm.algebra.structure
A Banach space, B, is a complete normed vector space such that every Cauchy sequence (with respect to the metric d(x, y) = |x - y|) in B has a limit in B.
Bartlett - Class in dev.nm.stat.test.variance
Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity.
Bartlett(double[]...) - Constructor for class dev.nm.stat.test.variance.Bartlett
Perform the Bartlett test to test for equal variances across the groups.
BARTLETT - dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
Bartlett's (1937) weighted least-squares scores
base() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
Get the radix or base of the coefficient.
BASIC - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the basic variables, i.e., constraints
basis() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the kernel basis.
Basis - Class in dev.nm.algebra.linear.vector.doubles.operation
A basis is a set of linearly independent vectors spanning a vector space.
Basis(int, int) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Basis
Construct a vector that corresponds to the i-th dimension in Rn.
basisAndFreeVars() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the kernel basis and the associated free variables for each basis/column.
BaumWelch - Class in dev.nm.stat.hmm.discrete
This implementation trains an HMM model by observations using the Baum–Welch algorithm.
BaumWelch(int[], DiscreteHMM, int) - Constructor for class dev.nm.stat.hmm.discrete.BaumWelch
Constructs an HMM model by training an initial model using the Baum–Welch algorithm.
BBNode - Interface in dev.nm.misc.algorithm.bb
A branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths.
BEFORE - dev.nm.interval.IntervalRelation
X takes place before Y.
begin() - Method in class dev.nm.interval.Interval
Get the beginning of this interval.
beq() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
beq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
beq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
beq() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the values of the equality constraints: beq as in \(A_{eq}x = b_{eq}\).
beq() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
BernoulliTrial - Class in dev.nm.stat.random.rng.univariate
A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success, p, is the same every time the experiment is conducted.
BernoulliTrial(RandomNumberGenerator, double) - Constructor for class dev.nm.stat.random.rng.univariate.BernoulliTrial
Creates a new instance that uses the given RandomNumberGenerator to do the trial.
Best1Bin - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
The Best-1-Bin rule is the same as the Rand-1-Bin rule, except that it always pick the best candidate in the population to be the base.
Best1Bin(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
Construct an instance of Best1Bin.
Best2Bin - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
The Best-1-Bin rule always picks the best chromosome as the base.
Best2Bin(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
Construct an instance of Best2Bin.
Best2Bin.DeBest2BinCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
 
beta - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
β = 2 / v'v.
beta - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
β: the shape parameter
beta - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the value of the wave coefficient β
beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the value of the wave coefficient β
beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets β in the equation (also called thermal diffusivity in case of the heat equation).
beta() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the coefficient in the PDE (thermal diffusivity in case of the heat equation).
beta() - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the set of cointegrating factors, by columns.
beta() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Gets the GLM coefficients estimator, β^.
beta() - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
Gets the GLM coefficient estimator, β^.
beta() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
 
beta() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
 
beta() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
 
beta() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
 
beta() - Method in interface dev.nm.stat.regression.linear.LinearModel
Gets \(\hat{\beta}\) and statistics.
beta() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
 
beta() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
 
beta() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Gets the GARCH coefficient.
beta() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the GARCH coefficients.
beta() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
 
beta(int) - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the r-th cointegrating factor, counting from 1.
Beta - Class in dev.nm.analysis.function.special.beta
The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]
Beta() - Constructor for class dev.nm.analysis.function.special.beta.Beta
 
BetaDistribution - Class in dev.nm.stat.distribution.univariate
The beta distribution is the posterior distribution of the parameter p of a binomial distribution after observing α - 1 independent events with probability p and β - 1 with probability 1 - p, if the prior distribution of p is uniform.
BetaDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.BetaDistribution
Construct a Beta distribution.
betaHat() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
Gets the estimates of β, β^, as in E(Y) = μ = g-1(Xβ)
betaHat() - Method in class dev.nm.stat.regression.linear.glm.IWLS
 
betaHat() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
betaHat() - Method in class dev.nm.stat.regression.linear.LMBeta
Gets the coefficient estimates, β^.
BetaMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Beta distribution to model the observations.
BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
Constructs a Beta distribution for each state in the HMM model.
BetaMixtureDistribution(BetaMixtureDistribution.Lambda[], int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
Constructs a Beta distribution for each state in the HMM model.
BetaMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
the Beta distribution parameters
BetaRegularized - Class in dev.nm.analysis.function.special.beta
The Regularized Incomplete Beta function is defined as: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]
BetaRegularized(double, double) - Constructor for class dev.nm.analysis.function.special.beta.BetaRegularized
Construct an instance of Ix(p,q) with the parameters p and q.
BetaRegularizedInverse - Class in dev.nm.analysis.function.special.beta
The inverse of the Regularized Incomplete Beta function is defined at: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]
BetaRegularizedInverse(double, double) - Constructor for class dev.nm.analysis.function.special.beta.BetaRegularizedInverse
Construct an instance of \(I^{-1}_{(p,q)}(u)\) with parameters p and p.
betas() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the sequence of betas.
BFGSImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer.BFGSImpl
 
BFGSMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
BFGSMinimizer(boolean, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
Construct a multivariate minimizer using the BFGS method.
BFGSMinimizer.BFGSImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
an implementation of the BFGS algorithm
BFS<V> - Class in dev.nm.graph.algorithm.traversal
This class implements the breadth-first-search using iteration.
BFS(Graph<V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.BFS
Constructs a BFS tree of a graph.
BFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class dev.nm.graph.algorithm.traversal.BFS
Runs the breadth-first-search on a graph from a designated root.
BFS.Node<V> - Class in dev.nm.graph.algorithm.traversal
This is a node in a BFS-spanning tree.
bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
Computes the amount of deviation from neutrality, hence bias.
bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
Computes the amount of deviation from self financing, hence bias.
bias(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
Computes the amount of deviation from zero value, hence bias.
BIC() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
Gets the Bayesian information criterion.
BiconjugateGradientSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems.
BiconjugateGradientSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
Construct a Biconjugate Gradient (BiCG) solver.
BiconjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
Construct a Biconjugate Gradient (BiCG) solver.
BiconjugateGradientStabilizedSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems.
BiconjugateGradientStabilizedSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
BiconjugateGradientStabilizedSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
Construct a Biconjugate Gradient Stabilized solver (BiCGSTAB) .
BicubicInterpolation - Class in dev.nm.analysis.curvefit.interpolation.bivariate
Bicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation.
BicubicInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
Constructs a new instance which computes the partial derivatives using PartialDerivativesByCenteredDifferencing.
BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
Constructs a new instance which uses the given derivatives to interpolate.
BicubicInterpolation.PartialDerivatives - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
Specify the partial derivatives defined at points on a BivariateGrid.
BicubicSpline - Class in dev.nm.analysis.curvefit.interpolation.bivariate
Bicubic splines are the two-dimensional equivalent of cubic splines.
BicubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicSpline
 
BiDiagonalization - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
Given a tall (m x n) matrix A, where m ≥ n, find orthogonal matrices U and V such that U' * A * V = B.
BiDiagonalizationByGolubKahanLanczos - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.
BiDiagonalizationByGolubKahanLanczos(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByGolubKahanLanczos(Matrix, double, RandomLongGenerator) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByGolubKahanLanczos(Matrix, RandomLongGenerator) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.
BiDiagonalizationByHouseholder - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
Given a tall (m x n) matrix A, where m ≥ n, we find orthogonal matrices U and V such that U' * A * V = B.
BiDiagonalizationByHouseholder(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Runs the Householder bi-diagonalization for a tall matrix.
BidiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
A bi-diagonal matrix is either upper or lower diagonal.
BidiagonalMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Constructs a bi-diagonal matrix from a 2D double[][] array.
BidiagonalMatrix(int, BidiagonalMatrix.BidiagonalMatrixType) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Constructs a 0 bi-diagonal matrix of dimension dim * dim.
BidiagonalMatrix(BidiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Copy constructor.
BidiagonalMatrix.BidiagonalMatrixType - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
the available types of bi-diagonal matrices
BidiagonalSVDbyMR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3
Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
BidiagonalSVDbyMR3(BidiagonalMatrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
Creates a singular value decomposition for a bidiagonal matrix A.
BigDecimalUtils - Class in dev.nm.number.big
These are the utility functions to manipulate BigDecimal.
bigDecimalValue() - Method in class dev.nm.number.ScientificNotation
Convert the number to BigDecimal.
BigIntegerUtils - Class in dev.nm.number.big
These are the utility functions to manipulate BigInteger.
BilinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.bivariate
Bilinear interpolation is the 2-dimensional equivalent of linear interpolation.
BilinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
 
bin(MultinomialRVG) - Static method in class dev.nm.stat.markovchain.SimpleMC
Picks the first non-empty bin.
BinomialDistribution - Class in dev.nm.stat.distribution.univariate
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
BinomialDistribution(int, double) - Constructor for class dev.nm.stat.distribution.univariate.BinomialDistribution
Construct a Binomial distribution.
BinomialMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Binomial distribution to model the observations.
BinomialMixtureDistribution(BinomialMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
Constructs a Binomial distribution for each state in the HMM model.
BinomialMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
the Binomial distribution parameters
BinomialRNG - Class in dev.nm.stat.random.rng.univariate
This random number generator samples from the binomial distribution.
BinomialRNG(int, double) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG
Construct a random number generator to sample from the binomial distribution.
BinomialRNG(int, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG
Construct a random number generator to sample from the binomial distribution.
bins - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
bin allocation; the j(kappa) defined between equations 33 and 34 in paper 2016
Bins<T> - Class in dev.nm.misc.algorithm
This class divides the items based on their keys into a number of bins.
Bins(int) - Constructor for class dev.nm.misc.algorithm.Bins
Constructs an empty bin of valued items.
Bins(int, Map<Double, T>) - Constructor for class dev.nm.misc.algorithm.Bins
Constructs a bin with valued items.
BisectionRoot - Class in dev.nm.analysis.root.univariate
The bisection method repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing.
BisectionRoot() - Constructor for class dev.nm.analysis.root.univariate.BisectionRoot
Construct an instance with Constants.EPSILON as the tolerance and Integer.MAX_VALUE as the maximum number of iterations.
BisectionRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.BisectionRoot
Construct an instance with the tolerance for convergence and the maximum number of iterations.
BivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.bivariate
Implementation of BivariateGrid, backed by arrays.
BivariateArrayGrid(double[][], double[], double[]) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
Constructs a new grid with a given two-dimensional array of grid values, and values for grid line positions along the x-axis and the y-axis.
BivariateEVD - Interface in dev.nm.stat.evt.evd.bivariate
Bivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of two-dimensional iid random vectors.
BivariateEVDAsymmetricLogistic - Class in dev.nm.stat.evt.evd.bivariate
The bivariate asymmetric logistic model.
BivariateEVDAsymmetricLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
BivariateEVDAsymmetricLogistic(double, double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
BivariateEVDAsymmetricLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
BivariateEVDAsymmetricMixed - Class in dev.nm.stat.evt.evd.bivariate
The asymmetric mixed model.
BivariateEVDAsymmetricMixed(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
BivariateEVDAsymmetricMixed(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
BivariateEVDAsymmetricNegativeLogistic - Class in dev.nm.stat.evt.evd.bivariate
The bivariate asymmetric negative logistic model.
BivariateEVDAsymmetricNegativeLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
BivariateEVDAsymmetricNegativeLogistic(double, double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
BivariateEVDAsymmetricNegativeLogistic(double, double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
BivariateEVDBilogistic - Class in dev.nm.stat.evt.evd.bivariate
The bilogistic model.
BivariateEVDBilogistic(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
BivariateEVDBilogistic(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
BivariateEVDBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
BivariateEVDColesTawn - Class in dev.nm.stat.evt.evd.bivariate
The Coles-Tawn model.
BivariateEVDColesTawn(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
BivariateEVDColesTawn(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
BivariateEVDColesTawn(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
BivariateEVDHuslerReiss - Class in dev.nm.stat.evt.evd.bivariate
The Husler-Reiss model.
BivariateEVDHuslerReiss(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
BivariateEVDHuslerReiss(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
BivariateEVDHuslerReiss(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
BivariateEVDLogistic - Class in dev.nm.stat.evt.evd.bivariate
The bivariate logistic model.
BivariateEVDLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
BivariateEVDLogistic(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
BivariateEVDLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
BivariateEVDNegativeBilogistic - Class in dev.nm.stat.evt.evd.bivariate
The negative bilogistic model.
BivariateEVDNegativeBilogistic(double, double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
BivariateEVDNegativeBilogistic(double, double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
BivariateEVDNegativeLogistic - Class in dev.nm.stat.evt.evd.bivariate
The bivariate negative logistic model.
BivariateEVDNegativeLogistic(double) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
BivariateEVDNegativeLogistic(double, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
BivariateEVDNegativeLogistic(double, GeneralizedEVD, GeneralizedEVD) - Constructor for class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
BivariateGrid - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
A rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate grid of double values.
BivariateGridInterpolation - Interface in dev.nm.analysis.curvefit.interpolation.bivariate
A bivariate interpolation, which requires the input to form a rectilinear grid.
BivariateProbabilityDistribution - Interface in dev.nm.stat.distribution.multivariate
A bivariate or joint probability distribution for X_1, X_2 is a probability distribution that gives the probability that each of X_1, X_2, ... falls in any particular range or discrete set of values specified for that variable.
BivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1
A bivariate real function takes two real arguments and outputs one real value.
BivariateRegularGrid - Class in dev.nm.analysis.curvefit.interpolation.bivariate
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.g.
BivariateRegularGrid(double[][], double, double, double, double) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Constructs a new grid where the dependent variable values are taken from the given two-dimensional array and the values of the dependent variables are specified by their first values and the difference between successive values.
BLACK - dev.nm.graph.algorithm.traversal.DFS.Node.Color
seen and done
BlockSplitPointSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes the splitting points with the given threshold.
BlockSplitPointSearch(double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
 
BlockWinogradAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
This implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication.
BlockWinogradAlgorithm() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
 
BMSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
A Brownian motion is a stochastic process with the following properties.
BMSDE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
Construct a univariate standard Brownian motion.
BMSDE(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
Construct a univariate Brownian motion.
BoltzAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
Matlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random.
BoltzAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
Constructs a new instance where the RVG is created from a given RLG.
BoltzAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
Constructs a new instance that uses a given RVG.
BOLTZMANN_K - Static variable in class dev.nm.misc.PhysicalConstants
The Boltzmann constant \(k\) in joule per kelvin (J K-1).
BoltzTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
\(T_k = T_0 / ln(k)\).
BoltzTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
Constructs a new instance with an initial temperature.
BootstrapEstimator - Class in dev.nm.stat.random.sampler.resampler
This class estimates the statistic of a sample using a bootstrap method.
BootstrapEstimator(Resampler, StatisticFactory, int) - Constructor for class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
Constructs a bootstrap estimator.
BootstrapEstimator(Resampler, StatisticFactory, int, boolean) - Constructor for class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
Constructs a bootstrap estimator.
BorderedHessian - Class in dev.nm.analysis.differentiation.multivariate
A bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g.
BorderedHessian(RealScalarFunction, RealScalarFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.BorderedHessian
Construct the bordered Hessian matrix for multivariate functions f and g at point x.
BottomUp<V> - Class in dev.nm.graph.algorithm.traversal
This implementation traverses a directed acyclic graph starting from the leaves at the bottom, and reaches the roots.
BottomUp(DAGraph<V, ? extends Arc<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.BottomUp
Constructs a BottomUp traversal instance.
bound(double, double, double) - Static method in class dev.nm.number.DoubleUtils
Bounds a given value by a given range.
Bound(int, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
Construct a bound constraint for a variable.
bounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Get a deep copy of the bounds.
BoxConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This represents the lower and upper bounds for a variable.
BoxConstraints(int, BoxConstraints.Bound...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Construct a set of bound constraints.
BoxConstraints(Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Construct a set of bound constraints.
BoxConstraints.Bound - Class in dev.nm.solver.multivariate.constrained.constraint.linear
A bound constraint for a variable.
BoxGeneralizedSimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.constrained.general.box
This is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions.
BoxGeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the boxed Generalized Simulated Annealing minimizer.
BoxGSAAcceptanceProbabilityFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
This probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0.
BoxGSAAcceptanceProbabilityFunction(Vector, Vector, double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.BoxGSAAcceptanceProbabilityFunction
Constructs a boxed acceptance probability function.
BoxGSAAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
 
BoxGSAAnnealingFunction(Vector, Vector, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
Constructs a boxed annealing function.
BoxMinimizer<P extends BoxOptimProblem,​S extends MinimizationSolution<?>> - Interface in dev.nm.solver.multivariate.constrained
A box minimizer solves a BoxOptimProblem.
BoxMuller - Class in dev.nm.stat.random.rng.univariate.normal
The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958) is a pseudo-random number sampling method for generating pairs of independent standard normally distributed (zero expectation, unit variance) random numbers, given a source of uniformly distributed random numbers.
BoxMuller() - Constructor for class dev.nm.stat.random.rng.univariate.normal.BoxMuller
Construct a random number generator to sample from the standard Normal distribution.
BoxMuller(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.BoxMuller
Construct a random number generator to sample from the standard Normal distribution.
BoxOptimProblem - Class in dev.nm.solver.multivariate.constrained.problem
A box constrained optimization problem, for which a solution must be within fixed bounds.
BoxOptimProblem(RealScalarFunction, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
Constructs an optimization problem with box constraints.
BoxOptimProblem(RealScalarFunction, BoxConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
Constructs an optimization problem with box constraints.
BoxOptimProblem(BoxOptimProblem) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
Copy constructor.
BoxPierce - Class in dev.nm.stat.test.timeseries.portmanteau
Deprecated.
BoxPierce(double[], int, int) - Constructor for class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
Deprecated.
Perform the Box-Pierce test to check auto-correlation in a time series.
BracketSearchMinimizer - Class in dev.nm.root.univariate.bracketsearch
This class provides implementation support for those univariate optimization algorithms that are based on bracketing.
BracketSearchMinimizer(double, int) - Constructor for class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
Construct a univariate minimizer using a bracket search method.
BracketSearchMinimizer.Solution - Class in dev.nm.root.univariate.bracketsearch
 
BranchAndBound - Class in dev.nm.misc.algorithm.bb
Branch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization.
BranchAndBound(ActiveList, BBNode) - Constructor for class dev.nm.misc.algorithm.bb.BranchAndBound
Solve a minimization problem using a branch-and-bound algorithm.
BranchAndBound(BBNode) - Constructor for class dev.nm.misc.algorithm.bb.BranchAndBound
Solve a minimization problem using a branch-and-bound algorithm using depth-first search.
branching() - Method in interface dev.nm.misc.algorithm.bb.BBNode
Get the children of this node by using the branching operation.
branching() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
Get the children of this node by using the branching operation.
BREAKDOWN - dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
Thrown when the iterative algorithm fails to proceed during its iterations, due to, for example, division-by-zero.
BrentCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Searches for the maximal point of C(η) by Brent's method.
BrentCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
Constructs a maximizer using the default epsilon (for the Brent's search algorithm).
BrentCetaMaximizer(double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
Constructs a maximizer with a given ε (for the Brent's search algorithm).
BrentMinimizer - Class in dev.nm.root.univariate.bracketsearch
Brent's algorithm is the preferred method for finding the minimum of a univariate function.
BrentMinimizer(double, int) - Constructor for class dev.nm.root.univariate.bracketsearch.BrentMinimizer
Construct a univariate minimizer using Brent's algorithm.
BrentMinimizer.Solution - Class in dev.nm.root.univariate.bracketsearch
This is the solution to a Brent's univariate optimization.
BrentRoot - Class in dev.nm.analysis.root.univariate
Brent's root-finding algorithm combines super-linear convergence with reliability of bisection.
BrentRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.BrentRoot
Construct an instance of Brent's root finding algorithm.
BreuschPagan - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
The Breusch-Pagan test tests for conditional heteroskedasticity.
BreuschPagan(LMResiduals, boolean) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
BrownForsythe - Class in dev.nm.stat.test.variance
The Brown-Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable.
BrownForsythe(double[]...) - Constructor for class dev.nm.stat.test.variance.BrownForsythe
Perform the Brown-Forsythe test to test for equal variances across the groups.
BruteForce<D,​R> - Class in dev.nm.misc.algorithm
A brute force algorithm, or brute-force search or exhaustive search, also known as generate and test, is a very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement.
BruteForce(Function<D, R>) - Constructor for class dev.nm.misc.algorithm.BruteForce
Constructs a brute force search for a function.
BruteForceIPMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.bruteforce
This implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations.
BruteForceIPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
Constructs a brute force minimizer to solve integral constrained minimization problems.
BruteForceIPMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
Constructs a brute force minimizer to solve integral constrained minimization problems.
BruteForceIPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.integer.bruteforce
This is the solution to an integral constrained minimization using the brute-force search.
BruteForceIPProblem - Class in dev.nm.solver.multivariate.constrained.integer.bruteforce
This implementation is an integral constrained minimization problem that has enumerable integral domains.
BruteForceIPProblem(RealScalarFunction, EqualityConstraints, LessThanConstraints, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
Construct an integral constrained minimization problem with explicit integral domains.
BruteForceIPProblem(RealScalarFunction, BruteForceIPProblem.IntegerDomain[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
Construct an integral constrained minimization problem with explicit integral domains.
BruteForceIPProblem.IntegerDomain - Class in dev.nm.solver.multivariate.constrained.integer.bruteforce
This specifies the integral domain for an integral variable, i.e., the integer values the variable can take.
BruteForceMinimizer<R extends Comparable<R>> - Class in dev.nm.solver.multivariate.unconstrained
This implementation solves an unconstrained minimization problem by brute force search for all given possible values.
BruteForceMinimizer(boolean) - Constructor for class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer
 
BruteForceMinimizer.Solution - Class in dev.nm.solver.multivariate.unconstrained
This is the solution to solving an optimization using the brute force algorithm.
Bt - Class in dev.nm.stat.stochasticprocess.univariate.filtration
This is a FiltrationFunction that returns \(B(t_i)\), the Brownian motion value at the i-th time point.
Bt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.Bt
 
Bt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the entire Brownian path.
build() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
 
build(double) - Method in class tech.nmfin.meanreversion.hvolatility.Kagi
Makes a KAGI construction for the given random process.
Builder(Dai2011HMM, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
 
BurlischStoerExtrapolation - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation
Burlisch-Stoer extrapolation (or Gragg-Bulirsch-Stoer (GBS)) algorithm combines three powerful ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-type applications, and the modified midpoint method, to obtain numerical solutions to ordinary differential equations (ODEs) with high accuracy and comparatively little computational effort.
BurlischStoerExtrapolation(double, int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
Create an instance of the algorithm with the precision parameter and the maximum number of iterations allowed.
BurnInRNG - Class in dev.nm.stat.random.rng.univariate
A burn-in random number generator discards the first M samples.
BurnInRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.univariate.BurnInRNG
Construct a burn-in RNG.
BurnInRVG - Class in dev.nm.stat.random.rng.multivariate
A burn-in random number generator discards the first M samples.
BurnInRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.BurnInRVG
Construct a burn-in RVG.
BY_INDEX - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
This Comparator sorts the vector entries by their indices.

C

c - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
n=c/p
c() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets the value of c.
c() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
Get c.
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
 
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets c.
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
 
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
 
c() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Get the objective function.
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
c() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
c() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
c() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
c(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets ci.
c(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
 
C() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets C.
C() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets C.
C() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets C as in eq.
c_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
c_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
c_l_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
c_q(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets \(c^q_i\).
c_q_full() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
c_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
C0() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the covariance matrix of x0.
C0() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the variance of x0.
c1() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the coefficient c1 in the mixed boundary condition at the boundary x = 0.
c1() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the coefficient c1 in the mixed boundary condition at the boundary x = 0.
C1 - Interface in dev.nm.analysis.differentiation.differentiability
A function, f, is said to be of class C1 if the derivative f' exists.
c2() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the coefficient c2 in the mixed boundary condition at the boundary x = a.
c2() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the coefficient c2 in the mixed boundary condition at the boundary x = a.
C2 - Interface in dev.nm.analysis.differentiation.differentiability
A function, f, is said to be of class C2 if the first and second derivatives, f' and f'', exist.
C2OptimProblem - Interface in dev.nm.solver.problem
This is an optimization problem of a real valued function that is twice differentiable.
C2OptimProblemImpl - Class in dev.nm.solver.problem
This is an optimization problem of a real valued function: \(\max_x f(x)\).
C2OptimProblemImpl(RealScalarFunction) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(RealScalarFunction, RealVectorFunction) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(RealScalarFunction, RealVectorFunction, RntoMatrix) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
Construct an optimization problem with an objective function.
C2OptimProblemImpl(C2OptimProblemImpl) - Constructor for class dev.nm.solver.problem.C2OptimProblemImpl
Copy Ctor.
CalibrationParam(double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
CartesianProduct<T> - Class in dev.nm.misc.algorithm
The Cartesian product can be generalized to the n-ary Cartesian product over n sets X1, ..., Xn.
CartesianProduct(T[]...) - Constructor for class dev.nm.misc.algorithm.CartesianProduct
Construct an Iterable of all combinations of arrays, taking one element from each array.
CaseResamplingReplacement - Class in dev.nm.stat.random.sampler.resampler.bootstrap
This is the classical bootstrap method described in the reference.
CaseResamplingReplacement(double[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CaseResamplingReplacement(double[], ConcurrentCachedRLG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CaseResamplingReplacement(double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
Constructs a bootstrap sample generator.
CaseResamplingReplacementForObject<X> - Class in dev.nm.stat.random.sampler.resampler.bootstrap
This is the classical bootstrap method described in the reference.
CaseResamplingReplacementForObject(X[], Class<X>) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
Constructs a bootstrap sample generator.
CaseResamplingReplacementForObject(X[], Class<X>, ConcurrentCachedRLG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
Constructs a bootstrap sample generator.
CaseResamplingReplacementForObject(X[], Class<X>, RandomLongGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
Constructs a bootstrap sample generator.
CATMULL_ROM - dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
Catmull-Rom splines are a special case of Cardinal splines and are defined as: \[ (\frac{\partial y}{\partial x})_k = \frac{y_{k+1} - y_{k-1}}{x_{k+1} - x_{k-1}}.
CauchyPolynomial - Class in dev.nm.analysis.function.polynomial
The Cauchy's polynomial of a polynomial takes this form:
CauchyPolynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.CauchyPolynomial
 
cbind(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of matrices by columns.
cbind(SparseMatrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse matrices by columns.
cbind(SparseVector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse vectors by columns and returns a CSR sparse matrix.
cbind(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of vectors by columns.
cbind(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines a list of vectors by columns.
ccdf(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
ccdf(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
The complementary cumulative distribution function.
cdf(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
cdf(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
cdf(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
The cumulative distribution function.
cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
The cumulative distribution function.
cdf(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
 
cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
 
cdf(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
 
cdf(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
 
cdf(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
cdf(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
cdf(double, double) - Method in interface dev.nm.stat.distribution.multivariate.BivariateProbabilityDistribution
The joint distribution function \(F_{X_1,X_2}(x_1,x_2) = Pr(X_1 \le x_1, X_2 \le x_2)\).
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
cdf(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
 
cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
cdf(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
Gets the cumulative probability F(x) = Pr(X ≤ x).
cdf(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
CENTRAL - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
central difference
centralMoment(int) - Method in class dev.nm.stat.descriptive.moment.Moments
Get the value of the k-th central moment.
CentralPath - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
A central path is a solution to both the primal and dual problems of a semi-definite programming problem.
CentralPath(Matrix, Vector, Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
Construct a central path.
Ceta - Class in tech.nmfin.portfoliooptimization.lai2010.ceta
The function C(η) to be maximized (Eq.
Ceta(Ceta.PortfolioMomentsEstimator, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta
 
Ceta.PortfolioMoments - Class in tech.nmfin.portfoliooptimization.lai2010.ceta
 
Ceta.PortfolioMomentsEstimator - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta
 
CetaMaximizer - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Defines an algorithm to search for the maximal C(η).
CetaMaximizer.NegCetaFunction - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
 
CetaMaximizer.Solution - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
 
ChangeOfVariable - Class in dev.nm.analysis.integration.univariate.riemann
Change of variable can easy the computation of some integrals, such as improper integrals.
ChangeOfVariable(SubstitutionRule, Integrator) - Constructor for class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
Construct an integrator that uses change of variable to do integration.
CHARACTERISTIC_IMPEDANCE_Z0 - Static variable in class dev.nm.misc.PhysicalConstants
The characteristic impedance of vacuum \(Z_0\) in ohms (Ω).
CHARACTERISTIC_POLYNOMIAL - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
For a matrix of dimension 4 or smaller.
CharacteristicPolynomial - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
The characteristic polynomial of a square matrix is the function
CharacteristicPolynomial(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
Construct the characteristic polynomial for a square matrix.
ChebyshevRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
 
ChebyshevRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
Create a Chebyshev rule of the given order.
checkInputs() - Method in class dev.nm.stat.regression.linear.LMProblem
Checks whether this LMProblem instance is valid.
checkInputs() - Method in class dev.nm.stat.regression.linear.logistic.LogisticProblem
 
Cheng1978 - Class in dev.nm.stat.random.rng.univariate.beta
Cheng, 1978, is a new rejection method for generating beta variates.
Cheng1978(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
Constructs a random number generator to sample from the beta distribution.
Cheng1978(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978
Constructs a random number generator to sample from the beta distribution.
children(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
children(V) - Method in interface dev.nm.graph.DiGraph
Gets the set of all children of this vertex.
children(V) - Method in class dev.nm.graph.type.SparseDiGraph
 
children(V) - Method in class dev.nm.graph.type.SparseTree
 
ChiSquareDistribution - Class in dev.nm.stat.distribution.univariate
The Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables.
ChiSquareDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.ChiSquareDistribution
Construct a Chi-Square distribution.
ChiSquareIndependenceTest - Class in dev.nm.stat.test.distribution.pearson
Pearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other.
ChiSquareIndependenceTest(Matrix) - Constructor for class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
Assess whether the two random variables in the contingency table are independent.
ChiSquareIndependenceTest(Matrix, int, ChiSquareIndependenceTest.Type) - Constructor for class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
Assess whether the two random variables in the contingency table are independent.
ChiSquareIndependenceTest.Type - Enum in dev.nm.stat.test.distribution.pearson
the available distributions used for the test
Chol - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
Chol(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Chol(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Chol(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
Cholesky - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskyBanachiewicz - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskyBanachiewicz(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
CholeskyBanachiewiczParallelized - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
This is a parallelized version of CholeskyBanachiewicz.
CholeskyBanachiewiczParallelized(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
 
CholeskySparse - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.
CholeskySparse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.
CholeskyWang2006 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Cholesky decomposition works only for a positive definite matrix.
CholeskyWang2006(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
Constructs the Cholesky decomposition of a matrix.
Chromosome - Interface in dev.nm.solver.multivariate.geneticalgorithm
A chromosome is a representation of a solution to an optimization problem.
CIRCULAR - dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
 
clamped() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
Constructs an instance with end conditions which fits clamped splines, and the first derivative at both ends are zero.
clamped(double, double) - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
Constructs an instance with end conditions which fits clamped splines, meaning that the first derivative at both ends equal to the given values.
clear() - Method in interface dev.nm.misc.algorithm.bb.ActiveList
Removes all of the elements from this collection.
clear() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
CLOSED - dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
The first and the last terms in the Euler-Maclaurin formula are included in the sum.
Cluster(int, int) - Constructor for class dev.nm.stat.evt.cluster.Clusters.Cluster
Create a cluster with the beginning and ending indices of the cluster.
ClusterAnalyzer - Class in dev.nm.stat.evt.cluster
This class counts clusters of exceedances based on observations above a given threshold, and the discontinuity of exceedances can be tolerated by an interval length r.
ClusterAnalyzer(double) - Constructor for class dev.nm.stat.evt.cluster.ClusterAnalyzer
Create an instance with the given threshold value and default interval length value of 1.
ClusterAnalyzer(double, int) - Constructor for class dev.nm.stat.evt.cluster.ClusterAnalyzer
Create an instance with the given threshold and clustering interval length.
clusters() - Method in class dev.nm.graph.community.GirvanNewman
Gets all the clusters, each of which is connected.
Clusters - Class in dev.nm.stat.evt.cluster
Store cluster information obtained by cluster analysis.
Clusters(double[], List<Clusters.Cluster>, int) - Constructor for class dev.nm.stat.evt.cluster.Clusters
 
Clusters.Cluster - Class in dev.nm.stat.evt.cluster
Define the beginning and ending indices (inclusively) of a cluster.
Coefficients(ConvectionDiffusionEquation1D, int, int, double[]) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
Constructs the coefficient computation
Coefficients(HeatEquation1D, int, double, double) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
Constructs the coefficient computation
CointegrationMLE - Class in dev.nm.stat.cointegration
Two or more time series are cointegrated if they each share a common type of stochastic drift, that is, to a limited degree they share a certain type of behavior in terms of their long-term fluctuations, but they do not necessarily move together and may be otherwise unrelated.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series, using the EIGEN test, with the number of lags = 2.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series, using the EIGEN test.
CointegrationMLE(MultivariateSimpleTimeSeries, boolean, int, Matrix) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE
Perform the Johansen MLE procedure on a multivariate time series.
collection2DoubleArray(Collection<? extends Number>) - Static method in class dev.nm.number.DoubleUtils
Convert a collection of numbers to a double array.
collection2IntArray(Collection<Integer>) - Static method in class dev.nm.number.DoubleUtils
Convert a collection of Integers to an int array.
collection2LongArray(Collection<Long>) - Static method in class dev.nm.number.DoubleUtils
Convert a collection of Longs to a long array.
colMeans(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column means.
colMeanVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column mean vector of a given matrix.
color - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
 
color() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
Gets the color of this node.
colSums(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column sums.
colSumVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the column sum vector of a given matrix.
ColumnBindMatrix - Class in dev.nm.algebra.linear.matrix.doubles.operation
A fast "cbind" matrix from vectors.
ColumnBindMatrix(Vector...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
columnIndices() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
 
columns(Matrix, int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the columns of a matrix.
columns(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the columns of a matrix.
combination(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
Compute the combination function or binomial coefficient.
combination(int, int) - Static method in class dev.nm.number.big.BigIntegerUtils
Compute the combination function or the binomial coefficient.
CombinedCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Searches the maximum C(η) by an array of given maximizers, being tried in sequence.
CombinedCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
Constructs a combined maximizer.
CombinedCetaMaximizer(CetaMaximizer[]) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
Constructs a combined maximizer.
CombinedVectorByRef - Class in dev.nm.algebra.linear.vector.doubles
For efficiency, this wrapper concatenates two or more vectors by references (without data copying).
CombinedVectorByRef(Vector, Vector, Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
 
commission(double) - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
 
commission(double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
 
CommonRandomNumbers - Class in dev.nm.stat.random.variancereduction
The common random numbers is a variance reduction technique to apply when we are comparing two random systems, e.g., \(E(f(X_1) - g(X_2))\).
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.random.variancereduction.CommonRandomNumbers
Estimate \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random variables.
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator) - Constructor for class dev.nm.stat.random.variancereduction.CommonRandomNumbers
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random variables.
CommonRandomNumbers(UnivariateRealFunction, UnivariateRealFunction, RandomLongGenerator, UnivariateRealFunction) - Constructor for class dev.nm.stat.random.variancereduction.CommonRandomNumbers
Estimates \(E(f(X_1) - g(X_2))\), where f and g are functions of uniform random variables.
compare(double, double, double) - Static method in class dev.nm.number.DoubleUtils
Compares two doubles up to a precision.
compare(Pair, Pair) - Method in class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
 
compare(Pair, Pair) - Method in class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
 
compare(Number, double) - Method in class dev.nm.number.complex.Complex
 
compare(Number, double) - Method in interface dev.nm.number.NumberUtils.Comparable
Compare this and that numbers up to a precision.
compare(Number, Number, double) - Static method in class dev.nm.number.NumberUtils
Compare two numbers.
compare(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compare two BigDecimals up to a precision.
comparePeriods(Period, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Compares two periods, assuming 30 days per month.
comparePeriods(Period, Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Compares two periods, with the assumed number of days per month.
compareTo(Pair) - Method in class dev.nm.analysis.function.tuple.Pair
 
compareTo(GraphTraversal.Node<V>) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
 
compareTo(SortableArray) - Method in class dev.nm.misc.datastructure.SortableArray
 
compareTo(Real) - Method in class dev.nm.number.Real
 
compareTo(BoxConstraints.Bound) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
 
compareTo(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
 
compareTo(PanelData.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
 
Complex - Class in dev.nm.number.complex
A complex number is a number consisting of a real number part and an imaginary number part.
Complex(double) - Constructor for class dev.nm.number.complex.Complex
Construct a complex number from a real number.
Complex(double, double) - Constructor for class dev.nm.number.complex.Complex
Construct a complex number from the real and imaginary parts.
ComplexMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
This is a Complex matrix.
ComplexMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
ComplexMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
ComplexMatrix(Complex[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a Complex matrix.
CompositeDoubleArrayOperation - Class in dev.nm.number.doublearray
It is desirable to have multiple implementations and switch between them for, e.g., performance reason.
CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
Construct a CompositeDoubleArrayOperation that chooses an implementation by array length.
CompositeDoubleArrayOperation(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation
Construct a CompositeDoubleArrayOperation by supplying the multiplexing criterion and the multiple DoubleArrayOperations.
CompositeDoubleArrayOperation.ImplementationChooser - Interface in dev.nm.number.doublearray
Specify which implementation to use.
CompositeLinearCongruentialGenerator - Class in dev.nm.stat.random.rng.univariate.uniform.linear
A composite generator combines a number of simple LinearCongruentialGenerator, such as Lehmer, to form one longer period generator by first summing values and then taking modulus.
CompositeLinearCongruentialGenerator(LinearCongruentialGenerator[]) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
Constructs a linear congruential generator from some simpler and shorter modulus generators.
compute() - Method in class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
 
compute(double[][], double[][], int) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
Compute the statistics.
compute(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion.MatrixNorm
 
compute(Matrix) - Method in class dev.nm.stat.covariance.LedoitWolf2004
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the optimal shrinkage parameter computed by the algorithm.
compute(Matrix, double) - Method in class dev.nm.stat.covariance.LedoitWolf2004
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the given shrinkage parameter.
compute(Vector, Vector, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.BlockSplitPointSearch
Searches splitting points in the symmetric tridiagonal matrix.
computeCointegratingBeta(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
computeGershgorinIntervals(Vector, Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
Computes the Gershgorin bounds for all eigenvalues in a symmetric tridiagonal matrix T.
computeOptimalPositions(int) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
 
computeOptimalPositions(int) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Computes the solution to the problem described in Section 3.2 in reference.
computeOptimalPositions(int) - Method in interface tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueSolver
Computes the solution to the problem described in Section 3.2 in reference.
computePrice(double, double) - Method in interface tech.nmfin.returns.ReturnsCalculator
Computes the next price after a return.
computePrice(double, double) - Method in enum tech.nmfin.returns.ReturnsCalculators
 
computeReturn(double, double) - Method in interface tech.nmfin.returns.ReturnsCalculator
Computes the portfolio return.
computeReturn(double, double) - Method in enum tech.nmfin.returns.ReturnsCalculators
 
computeRobustPair(String, String, Vector, Vector) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
computeShortTermCointegratingBeta(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
computeWeightedRSS(ACERFunction.ACERParameter, double[], double[], double[]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
Measure how fit the estimated log-ACER function to the empirical epsilons by weighted sum of squared residuals (RSS).
computeWeights(double[], double[]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
Compute weights from epsilon values and their corresponding confidence interval half-width.
computeWeightsByPeriodLength(double[][]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils
Compute the weights for periods, proportional to the lengths of the periods.
concat(double[]...) - Static method in class dev.nm.number.DoubleUtils
Concatenate an array of arrays into one array.
concat(SparseVector...) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of sparse vectors into one sparse vector.
concat(Vector...) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of vectors into one vector.
concat(LinearConstraints...) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Concatenate collections of linear constraints into one collection.
concat(Collection<Vector>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Concatenates an array of vectors into one vector.
CONCURRENT_RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
ConcurrentCachedGenerator<T> - Class in dev.nm.stat.random.rng.concurrent.cache
A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list.
ConcurrentCachedGenerator(ConcurrentCachedGenerator.Generator<T>, int) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator
Creates a new instance which wraps the given item generator and uses a cache of the specified size.
ConcurrentCachedGenerator.Generator<T> - Interface in dev.nm.stat.random.rng.concurrent.cache
Defines a generic generator of type T.
ConcurrentCachedRLG - Class in dev.nm.stat.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random long generators.
ConcurrentCachedRLG(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
Construct a new instance which wraps the given random long generator and uses a cache which has 1000 entries per available core.
ConcurrentCachedRLG(RandomLongGenerator, int) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
Constructs a new instance which wraps the given random long generator and uses a cache of the specified size.
ConcurrentCachedRNG - Class in dev.nm.stat.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random number generators.
ConcurrentCachedRNG(RandomNumberGenerator) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
Construct a new instance which wraps the given random number generator and uses a cache which has 8 entries per available core.
ConcurrentCachedRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
Constructs a new instance which wraps the given random number generator and uses a cache of the specified size.
ConcurrentCachedRVG - Class in dev.nm.stat.random.rng.concurrent.cache
This is a fast thread-safe wrapper for random vector generators.
ConcurrentCachedRVG(RandomVectorGenerator) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
Constructs a new instance which wraps the given random vector generator and uses a cache which has 8 entries per available core.
ConcurrentCachedRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
Constructs a new instance which wraps the given random vector generator and uses a cache of the specified size.
ConcurrentStandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
 
ConcurrentStandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
 
ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
 
conditionalCopula(double, double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD
The conditional copula function conditioning on either margin.
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
conditionalCopula(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class dev.nm.misc.parallel.ParallelExecutor
Calls forEach only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
Runs a parallel for-loop only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
Calls conditionalForLoop with increment of 1.
conditionalMean(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Compute the univariate AR1 conditional mean, given the last lag.
conditionalMean(double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Compute the univariate ARMA conditional mean, given all the lags.
conditionalMean(Matrix, Matrix) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Compute the multivariate ARMA conditional mean, given all the lags.
ConditionalSumOfSquares - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares.
ConditionalSumOfSquares(double[], int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Fit an ARIMA model for the observations using CSS.
ConditionalSumOfSquares(double[], int, int, int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Fit an ARIMA model for the observations using CSS.
conditionNumber(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Computes the condition number of a given matrix A.
CONDUCTANCE_QUANTUM_G0 - Static variable in class dev.nm.misc.PhysicalConstants
The conductance quantum \(G_0\) in siemens (s).
confidenceInterval(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Compute the \((1 - \alpha)100\%\) confidence intervals for each element of the fitted parameter, given the required confidence level.
confidenceInterval(double) - Method in class dev.nm.stat.test.mean.T
Get the confidence interval.
confidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
Compute the confidence interval.
ConfidenceInterval - Class in dev.nm.stat.evt.evd.univariate.fitting
This class stores information for a list of confidence intervals, with the same confidence level.
ConfidenceInterval(double, Vector, Vector, Vector) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
Create an instance with the confidence interval information.
CongruentMatrix - Class in dev.nm.algebra.linear.matrix.doubles.operation
Given a matrix A and an invertible matrix P, we create the congruent matrix B s.t., B = P'AP
CongruentMatrix(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.CongruentMatrix
Constructs the congruent matrix B = P'AP.
conjugate() - Method in class dev.nm.number.complex.Complex
Get the conjugate of the complex number, namely, (a - bi).
ConjugateGradientMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
A conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another.
ConjugateGradientMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
Construct a multivariate minimizer using the Conjugate-Gradient method.
ConjugateGradientNormalErrorSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, the normal equation matrix AAt is symmetric and positive definite, and hence CG is applicable.
ConjugateGradientNormalErrorSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
Construct a Conjugate Gradient Normal Error (CGNE) solver.
ConjugateGradientNormalErrorSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
Construct a Conjugate Gradient Normal Error (CGNE) solver.
ConjugateGradientNormalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, the normal equation matrix AAt is symmetric and positive definite, and hence CG is applicable.
ConjugateGradientNormalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
ConjugateGradientNormalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
Construct a Conjugate Gradient Normal Residual method (CGNR) solver.
ConjugateGradientSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system.
ConjugateGradientSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
Construct a Conjugate Gradient (CG) solver.
ConjugateGradientSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
Construct a Conjugate Gradient (CG) solver.
ConjugateGradientSquaredSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system.
ConjugateGradientSquaredSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
Construct a Conjugate Gradient Squared (CGS) solver.
ConjugateGradientSquaredSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
Construct a Conjugate Gradient Squared (CGS) solver.
CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
This is trend type III: constant, no linear trend:
CONSTANT - dev.nm.stat.test.timeseries.adf.TrendType
test for a unit root with drift
CONSTANT_RESTRICTED_TIME - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
This is trend type IV: constant, restricted linear trend:
CONSTANT_TIME - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
This is trend type V: constant, linear trend:
CONSTANT_TIME - dev.nm.stat.test.timeseries.adf.TrendType
test for a unit root with drift and deterministic time trend
ConstantDriftVector - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
The class represents a constant drift function.
ConstantDriftVector(Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
Construct a constant drift function.
Constants - Class in dev.nm.misc
This class lists the global parameters and constants in this nmdev library.
ConstantSeeder<T extends Seedable> - Class in dev.nm.stat.random.rng
A wrapper that seeds each given seedable random number generator with the given seed(s).
ConstantSeeder(Iterable<T>, long...) - Constructor for class dev.nm.stat.random.rng.ConstantSeeder
Constructs a wrapper with the underlying RNGs and the seeds for seeding each RNG.
ConstantSigma1 - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
The class represents a constant diffusion coefficient function.
ConstantSigma1(Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
Construct a constant diffusion coefficient function.
ConstantSigma2 - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
Deprecated.
This implementation is slow. Use ConstantSigma1 instead.
ConstantSigma2(Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated.
Construct a constant diffusion coefficient function.
ConstrainedCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
 
ConstrainedCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
This defines a Differential Evolution operator that takes in account constraints.
ConstrainedCellFactory(DEOptimCellFactory) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
Construct an instance of a ConstrainedCellFactory that define the constrained Differential Evolution operators.
ConstrainedCellFactory.ConstrainedCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
A ConstrainedCell is a chromosome for a constrained optimization problem.
ConstrainedLASSObyLARS - Class in dev.nm.stat.regression.linear.lasso
This class solves the constrained form of LASSO by modified least angle regression (LARS) and linear interpolation: \[ \min_w \left \{ \left \| Xw - y \right \|_2^2 \right \}\) subject to \( \left \| w \right \|_1 \leq t \]
ConstrainedLASSObyLARS(ConstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear interpolation.
ConstrainedLASSObyLARS(ConstrainedLASSOProblem, boolean, boolean, double, int) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
Solves a constrained LASSO problem by modified least angle regression (LARS) and linear interpolation.
ConstrainedLASSObyQP - Class in dev.nm.stat.regression.linear.lasso
This class solves the constrained form of LASSO (i.e.\(\min_w \left \{ \left \| Xw - y \right \|_2^2 \right \}\) subject to \( \left \| w \right \|_1 \leq t \)) by transforming it into a single quadratic programming problem with (2 * m + 1) constraints, where m is the number of columns of the design matrix.
ConstrainedLASSObyQP(ConstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
Solves a constrained LASSO problem by transforming it into a single quadratic programming problem.
ConstrainedLASSOProblem - Class in dev.nm.stat.regression.linear.lasso
A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.
ConstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
Constructs a LASSO problem in the constrained form.
ConstrainedLASSOProblem(ConstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
Copy constructor.
ConstrainedMinimizer<P extends ConstrainedOptimProblem,​S extends MinimizationSolution<?>> - Interface in dev.nm.solver.multivariate.constrained
A constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem.
ConstrainedOptimProblem - Interface in dev.nm.solver.multivariate.constrained.problem
A constrained optimization problem takes this form.
ConstrainedOptimProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.problem
This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints.
ConstrainedOptimProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
Constructs a constrained optimization problem.
ConstrainedOptimProblemImpl1(ConstrainedOptimProblemImpl1) - Constructor for class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
Copy constructor.
ConstrainedOptimSubProblem - Interface in dev.nm.solver.multivariate.constrained
A constrained optimization sub-problem takes this form.
constraints - Variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
the constraint/cost functions
constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.MinimumWeights
 
constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoConstraints
 
constraints() - Method in class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoShortSelling
 
constraints() - Method in interface tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005.WeightsConstraint
Gets the less-than constraints on weights.
Constraints - Interface in dev.nm.solver.multivariate.constrained.constraint
A set of constraints for a (real-valued) optimization problem is a set of functions.
ConstraintsUtils - Class in dev.nm.solver.multivariate.constrained.constraint
These are the utility functions for manipulating Constraints.
ConstraintsUtils() - Constructor for class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
 
ConstraintViolationException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
Constructs a ConstraintViolationException.
ConstraintViolationException(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.ConstraintViolationException
Constructs a ConstraintViolationException with an error message.
containActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Check if the active set contains a certain index.
containInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Check if the inactive set contains a certain index.
contains(UndirectedEdge<V>) - Method in class dev.nm.graph.community.EdgeBetweeness
Checks if the graph contains an edge.
contains(LocalDateTimeInterval) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
contains(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
contains(LocalDate) - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
contains(LocalDateTime) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
Whether this time interval contains the given time.
contains(V) - Method in class dev.nm.graph.type.SparseGraph
Check if this graph contains a vertex.
containsAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
containsEdge(Graph<V, ?>, HyperEdge<V>) - Static method in class dev.nm.graph.GraphUtils
Returns true if this graph's edge collection contains e
containsVertex(Graph<V, ?>, V) - Static method in class dev.nm.graph.GraphUtils
Returns true if this graph's vertex collection contains v
ContextRNG<T> - Class in dev.nm.stat.random.rng.concurrent.context
This uniform number generator generates independent sequences of random numbers per context.
ContextRNG() - Constructor for class dev.nm.stat.random.rng.concurrent.context.ContextRNG
 
ContinuedFraction - Class in dev.nm.analysis.function.rn2r1.univariate
A continued fraction representation of a number has this form: \[ z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}} \] ai and bi can be functions of x, which in turn makes z a function of x.
ContinuedFraction(ContinuedFraction.Partials) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction(ContinuedFraction.Partials, double, int) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction(ContinuedFraction.Partials, int, int) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
Construct a continued fraction.
ContinuedFraction.MaxIterationsExceededException - Exception in dev.nm.analysis.function.rn2r1.univariate
RuntimeException thrown when the continued fraction fails to converge for a given epsilon before a certain number of iterations.
ContinuedFraction.Partials - Interface in dev.nm.analysis.function.rn2r1.univariate
This interface defines a continued fraction in terms of the partial numerators an, and the partial denominators bn.
ControlVariates - Class in dev.nm.stat.random.variancereduction
Control variates method is a variance reduction technique that exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown quantity.
ControlVariates(UnivariateRealFunction, UnivariateRealFunction, double, double, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.ControlVariates
Estimates \(E(f(X_1))\), where f is a function of a random variable.
ControlVariates.Estimator - Interface in dev.nm.stat.random.variancereduction
 
ConvectionDiffusionEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
The convection–diffusion equation is a combination of the diffusion and convection (advection) equations, and describes physical phenomena where particles, energy, or other physical quantities are transferred inside a physical system due to two processes: diffusion and convection.
ConvectionDiffusionEquation1D(BivariateRealFunction, BivariateRealFunction, BivariateRealFunction, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Constructs a convection-diffusion equation problem.
ConvergenceFailure - Exception in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
ConvergenceFailure(ConvergenceFailure.Reason) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
Construct an exception with reason.
ConvergenceFailure(ConvergenceFailure.Reason, String) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
Construct an exception with reason and error message.
ConvergenceFailure.Reason - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
the reasons for the convergence failure
convertToDerivativeFunction(RealVectorFunction, int) - Static method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Converts the given vector function to a first order derivative function.
cookDistances() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
Cook distances.
coordinates - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
the coordinates of this entry
copy2D(double[][]) - Static method in class dev.nm.number.DoubleUtils
Copies a 2D array.
cor(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
 
cor(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
 
correct(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
 
correlation() - Method in class dev.nm.stat.descriptive.covariance.Covariance
Get the correlation, i.e., Pearson's correlation coefficient.
CorrelationCheck - Class in tech.nmfin.meanreversion.cointegration.check
 
CorrelationCheck(Matrix, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
 
CorrelationCheck(Matrix, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
 
CorrelationMatrix - Class in dev.nm.stat.descriptive.correlation
The correlation matrix of n random variables X1, ..., Xn is the n × n matrix whose i,j entry is corr(Xi, Xj), the correlation between X1 and Xn.
CorrelationMatrix(Matrix) - Constructor for class dev.nm.stat.descriptive.correlation.CorrelationMatrix
Construct a correlation matrix from a covariance matrix.
Corvalan2005 - Class in tech.nmfin.portfoliooptimization.corvalan2005
This paper tackles the corner solution problem of many portfolio optimizers, by optimizing the portfolio diversification with some relaxation on the volatility σ and the expected return R of a given optimized (but non-diversified) portfolio.
Corvalan2005(double, double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
Constructs an instance of the Corvalan model.
Corvalan2005(Minimizer<? super ConstrainedOptimProblem, IterativeSolution<Vector>>, DiversificationMeasure, double, double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
Constructs an instance of the Corvalan model.
Corvalan2005.WeightsConstraint - Interface in tech.nmfin.portfoliooptimization.corvalan2005
Constraints on weights which are defined by a set of less-than constraints.
cos(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the cosine of a vector, element-by-element.
cos(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Cosine of a complex number.
cosec(double) - Static method in class dev.nm.geometry.TrigMath
Returns the cosecant of an angle.
cosh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Hyperbolic cosine of a complex number.
cost() - Method in class dev.nm.graph.type.SimpleArc
 
cost() - Method in class dev.nm.graph.type.SimpleEdge
 
cost() - Method in interface dev.nm.graph.WeightedEdge
Gets the cost or weight of this edge.
COST - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the cost function (the last row)
COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
cot(double) - Static method in class dev.nm.geometry.TrigMath
Returns the cotangent of an angle.
coth(double) - Static method in class dev.nm.geometry.TrigMath
Returns the hyperbolic cotangent of a hyperbolic angle.
count(double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
Counts the number of eigenvalues that are less than a given value x.
count(double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
Computes the Sturm count.
count(double) - Method in class dev.nm.combinatorics.Counter
Get the count, i.e., the number of occurrences, of a particular number.
count(double, double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange
Counts the number of eigenvalues of T that are in the given interval.
countEntriesInEachColumn(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Counts the number of entries in each column.
countEntriesInEachRow(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Counts the number of entries in each row.
Counter - Class in dev.nm.combinatorics
A counter keeps track of the number of occurrences of numbers.
Counter() - Constructor for class dev.nm.combinatorics.Counter
Construct a counter with no rounding.
Counter(int) - Constructor for class dev.nm.combinatorics.Counter
Construct a counter.
CountMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
This IterationMonitor counts the number of iterates generated, hence the number of iterations.
CountMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
 
CourantPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
This penalty function sums up the squared error penalties.
CourantPenalty(EqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
CourantPenalty(EqualityConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
CourantPenalty(EqualityConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
Construct a CourantPenalty penalty function from a collection of equality constraints.
cov() - Method in class dev.nm.stat.random.variancereduction.AntitheticVariates
 
cov() - Method in class dev.nm.stat.random.variancereduction.CommonRandomNumbers
Gets the covariance between f and g.
cov() - Method in class dev.nm.stat.random.variancereduction.ControlVariates
 
covariance() - Method in interface dev.nm.stat.covariance.covarianceselection.CovarianceSelectionSolver
Get the estimated Covariance matrix of the selection problem.
covariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Gets the estimated covariance matrix.
covariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
Get the estimated covariance matrix.
covariance() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
covariance() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
covariance() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
covariance() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
Gets the covariance matrix of this distribution.
covariance() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
covariance() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
covariance() - Method in class dev.nm.stat.regression.linear.glm.GLMBeta
 
covariance() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMBeta
 
covariance() - Method in class dev.nm.stat.regression.linear.LMBeta
Gets the covariance matrix of the coefficient estimates, β^.
covariance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticBeta
 
covariance() - Method in class dev.nm.stat.regression.linear.ols.OLSBeta
 
covariance() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Get the asymptotic covariance matrix of the estimators.
covariance() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the asymptotic covariance matrix of the estimated parameters, φ and θ.
covariance() - Method in class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
 
covariance(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
Get the covariance matrix for prediction errors for \(\hat{x}_{n+1}\), made at time n.
covariance(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
Get the covariance matrix for prediction errors for \(\hat{x}_{n+1}\), made at time n.
covariance(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
Get the covariance matrix for prediction errors at time t for x^t+1.
Covariance - Class in dev.nm.stat.descriptive.covariance
Covariance is a measure of how much two variables change together.
Covariance() - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
Construct an empty Covariance calculator.
Covariance(double[], double[]) - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
Construct a Covariance calculator, initialized with two samples.
Covariance(Covariance) - Constructor for class dev.nm.stat.descriptive.covariance.Covariance
Copy constructor.
CovarianceEstimation - Class in tech.nmfin.meanreversion.daspremont2008
Estimates the covariance matrix by maximum likelihood.
CovarianceEstimation(Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
Solves the maximum likelihood problem for covariance selection.
covarianceMatrix() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Get the covariance matrix, which is estimated as the inverse of negative Hessian matrix of the log-likelihood function valued at the fitted parameter.
CovarianceSelectionGLASSOFAST - Class in dev.nm.stat.covariance.covarianceselection.lasso
GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem.
CovarianceSelectionGLASSOFAST(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Solves the maximum likelihood problem for covariance selection.
CovarianceSelectionLASSO - Class in dev.nm.stat.covariance.covarianceselection.lasso
The LASSO approach of covariance selection.
CovarianceSelectionLASSO(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
Estimate the covariance matrix directly by using LASSO.
CovarianceSelectionLASSO(CovarianceSelectionProblem, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
Estimate the covariance matrix directly by using LASSO.
CovarianceSelectionProblem - Class in dev.nm.stat.covariance.covarianceselection
This class defines the covariance selection problem outlined in d'Aspremont (2008).
CovarianceSelectionProblem(Matrix, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Constructs a covariance selection problem.
CovarianceSelectionProblem(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Copy constructor.
CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Constructs a covariance selection problem from a multivariate time series.
CovarianceSelectionProblem(MultivariateTimeSeries, double, boolean) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Constructs a covariance selection problem from a multivariate time series.
CovarianceSelectionSolver - Interface in dev.nm.stat.covariance.covarianceselection
 
covers(double) - Static method in class dev.nm.geometry.TrigMath
Returns the coversed sine or coversine of an angle.
Cr - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
the crossover probability
CramerVonMises2Samples - Class in dev.nm.stat.test.distribution
This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value.
CramerVonMises2Samples(double[], double[]) - Constructor for class dev.nm.stat.test.distribution.CramerVonMises2Samples
Calculate the statistics and p-value of two sample Cramer-Von Mises test.
CrankNicolsonConvectionDiffusionEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
This class uses the Crank-Nicolson scheme to obtain a numerical solution of a one-dimensional convection-diffusion PDE.
CrankNicolsonConvectionDiffusionEquation1D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
Constructs a Crank-Nicolson solver for a 1 dimensional convection-diffusion PDE.
CrankNicolsonConvectionDiffusionEquation1D.Coefficients - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
Gets the coefficients of a discretized 1D convection-diffusion equation for each time step.
CrankNicolsonHeatEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
The Crank-Nicolson method is an algorithm for obtaining a numerical solution to parabolic PDE problems.
CrankNicolsonHeatEquation1D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
 
CrankNicolsonHeatEquation1D.Coefficients - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
Gets the coefficients of a discretized 1D heat equation for each time step.
crossover(Chromosome) - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome
Construct a Chromosome by crossing over a pair of chromosomes.
crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
 
crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
 
crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
 
crossover(Chromosome) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
Crossover by taking the midpoint.
csch(double) - Static method in class dev.nm.geometry.TrigMath
Returns the hyperbolic cosecant of a hyperbolic angle.
CSDPMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
Implements the CSDP algorithm for semidefinite programming problem with equality constraints.
CSDPMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
CSDPMinimizer(double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
CSDPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
 
CSRSparseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
The Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr).
CSRSparseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format.
CSRSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format.
CSRSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format by a list of non-zero entries.
CSRSparseMatrix(int, int, List<SparseMatrix.Entry>, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix in CSR format by a list of non-zero entries.
CSRSparseMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Constructs a sparse matrix from a matrix.
CSRSparseMatrix(CSRSparseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
Copy constructor.
CSV_SEPARATOR - Static variable in class dev.nm.number.DoubleUtils
The default separator for CSV file parsing.
Ctor2x2(double, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Same as new GivensMatrix(2, 1, 2, c, s).
CtorFromRho(int, int, int, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix from ρ.
CtorToRotateColumns(int, int, int, double, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that [a b] * G = [* 0].
CtorToRotateRows(int, int, int, double, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that G * [a b]t = [* 0]t.
CtorToZeroOutEntry(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that G * A has 0 in the [i,j] entry.
CtorToZeroOutEntryByTranspose(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix such that Gt * A has 0 in the [i,j] entry.
CubicHermite - Class in dev.nm.analysis.curvefit.interpolation.univariate
Cubic Hermite spline interpolation is a piecewise spline interpolation, in which each polynomial is in Hermite form which consists of two control points and two control tangents.
CubicHermite() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
Construct an instance with CubicHermite.Tangents.CATMULL_ROM as the method for computing tangents.
CubicHermite(CubicHermite.Tangent) - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
Construct an instance with the given method to compute tangents.
CubicHermite.Tangent - Interface in dev.nm.analysis.curvefit.interpolation.univariate
The method for computing the control tangent at a given index.
CubicHermite.Tangents - Enum in dev.nm.analysis.curvefit.interpolation.univariate
 
CubicRoot - Class in dev.nm.analysis.function.polynomial.root
This is a cubic equation solver.
CubicRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.CubicRoot
 
CubicSpline - Class in dev.nm.analysis.curvefit.interpolation.univariate
The cubic spline interpolation fits a cubic polynomial between each pair of adjacent points such that adjacent cubics are continuous in their first and second derivatives.
CubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
Constructs an instance with default end conditions which fits natural splines, meaning that the second derivative at both ends are zero.
cumsum(double[]) - Static method in class dev.nm.number.DoubleUtils
Gets the cumulative sums of the elements in an array.
cumsum(int[]) - Static method in class dev.nm.number.DoubleUtils
Gets the cumulative sums of the elements in an array.
cumsum(Vector[]) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the cumulative sums.
cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
cumulant(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
The cumulant function of the exponential distribution.
cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
cumulant(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
CumulativeNormalHastings - Class in dev.nm.analysis.function.special.gaussian
Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal.
CumulativeNormalHastings() - Constructor for class dev.nm.analysis.function.special.gaussian.CumulativeNormalHastings
 
CumulativeNormalInverse - Class in dev.nm.analysis.function.special.gaussian
The inverse of the cumulative standard Normal distribution function is defined as: \[ N^{-1}(u) /]
CumulativeNormalInverse() - Constructor for class dev.nm.analysis.function.special.gaussian.CumulativeNormalInverse
 
CumulativeNormalMarsaglia - Class in dev.nm.analysis.function.special.gaussian
Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal.
CumulativeNormalMarsaglia() - Constructor for class dev.nm.analysis.function.special.gaussian.CumulativeNormalMarsaglia
 
cumulativeProportionVar() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the cumulative proportion of overall variance explained by the principal components
CurveFitting - Interface in dev.nm.analysis.curvefit
Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints.
cut(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
 
cut(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
 
cut(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory.Cutter
Cut a simplex table.

D

d() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Gets d.
d() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the order of integration.
d() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the order of integration.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
Get the diagonal matrix D as in Q * D * Q' = A.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
The diagonal entries of the diagonal matrix D.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
 
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Gets the D matrix as in the real Schur canonical form Q'AQ = D.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
 
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
 
D() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
Get the D matrix as in SVD decomposition.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Returns the matrix D as in A=UDV'.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
Get D the the diagonal matrix in the LDL decomposition.
D() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Gets the diagonal matrix D in the LDL decomposition.
D() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
Computes D.
D() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets D as in eq.
D() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
 
DAgostino - Class in dev.nm.stat.test.distribution.normality
D'Agostino's K2 test is a goodness-of-fit measure of departure from normality.
DAgostino(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.DAgostino
Perform D'Agostino's test to test for the departure from normality.
DAGraph<V,​E extends Arc<V>> - Interface in dev.nm.graph
A directed acyclic graph (DAG), is a directed graph with no directed cycles.
Dai2011HMM - Class in tech.nmfin.trend.dai2011
Creates a two-state Geometric Brownian Motion with a constant volatility.
Dai2011HMM(double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
Constructs a two-state Markov switching Geometric Brownian Motion.
Dai2011HMM(Dai2011HMM) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
Copy constructor.
Dai2011HMM(Dai2011HMM.ModelParam) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
 
Dai2011HMM.CalibrationParam - Class in tech.nmfin.trend.dai2011
 
Dai2011HMM.ModelParam - Class in tech.nmfin.trend.dai2011
 
Dai2011Solver - Class in tech.nmfin.trend.dai2011
Solves the stochastic control problem in the referenced paper to get the two thresholds.
Dai2011Solver.Boundaries - Class in tech.nmfin.trend.dai2011
 
Dai2011Solver.Builder - Class in tech.nmfin.trend.dai2011
 
dampedBFGSHessianUpdate(Matrix, Vector, Vector) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
Damped BFGS Hessian update.
data() - Method in class dev.nm.graph.type.VertexTree
 
DATE_FORMAT_STRING - Static variable in class dev.nm.misc.license.License
Date format for all kinds of dates in a license file.
DateTimeGenericTimeSeries<V> - Class in dev.nm.stat.timeseries.datastructure
This is a generic time series where time is indexed by LocalDateTime and value can be any type.
DateTimeGenericTimeSeries(LocalDateTime[], V[]) - Constructor for class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
Construct a time series.
DateTimeGenericTimeSeries.Entry<V> - Class in dev.nm.stat.timeseries.datastructure
This is the TimeSeries.Entry for a DateTime -indexed time series.
DateTimeTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate
This is a time series has its double values indexed by LocalDateTime.
DateTimeTimeSeries(LocalDateTime[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
Construct a time series from LocalDateTime and double.
DateTimeTimeSeries(List<LocalDateTime>, List<Double>) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries
Construct a time series from LocalDateTime and double.
dayCount() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
db(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
\[ \frac{d\sigma}{dt} \]
dB(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Get a Brownian motion increment.
dB(double) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
Get a Brownian motion increment.
dB(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the Brownian increment at the i-th time point.
DBeta - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of the Beta function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\).
DBeta() - Constructor for class dev.nm.analysis.differentiation.univariate.DBeta
 
DBetaRegularized - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w.r.t the upper limit, x.
DBetaRegularized(double, double) - Constructor for class dev.nm.analysis.differentiation.univariate.DBetaRegularized
Construct the derivative function of the Regularized Incomplete Beta function, BetaRegularized.
dBt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get all the Brownian increments.
ddy() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
Gets y'' = F(x,y).
DeBest2BinCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
 
deColumnMean(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the de-mean (column means) matrix of a given matrix.
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
Make a deep copy of the underlying matrix.
deepCopy() - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
Return this as this Matrix is immutable.
deepCopy() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
Returns this as the reference is immutable.
deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
 
deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
deepCopy() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
deepCopy() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
 
deepCopy() - Method in interface dev.nm.misc.DeepCopyable
The implementation returns an instance created from this by the copy constructor of the class, or just this if the instance itself is immutable.
deepCopy() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
 
deepCopy() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
 
deepCopy() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
 
deepCopy() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
 
DeepCopyable - Interface in dev.nm.misc
This interface provides a way to do polymorphic copying.
DEFAULT - dev.nm.number.DoubleUtils.RoundingScheme
This rounding scheme is the same as in Math.round(float).
DEFAULT_CACHE_SIZE - Static variable in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
The default cache size = the number of available processors × 1000.
DEFAULT_CACHE_SIZE - Static variable in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
The default cache size = the number of available processors × 1000.
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
 
DEFAULT_EPSILON - Static variable in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
 
DEFAULT_GRID_SIZE - Static variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
 
DEFAULT_INITIAL_TEMPERATURE - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default initial temperature
DEFAULT_LICENSE_FILES - Static variable in class dev.nm.misc.license.License
Default license file names.
DEFAULT_MATRIX_SIZE_THRESHOLD - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
The default matrix size threshold.
DEFAULT_MAXIMUM_ITERATIONS - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
 
DEFAULT_MERSENNE_EXPONENT - Static variable in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
 
DEFAULT_METHOD - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
The default algorithm for computing SVD.
DEFAULT_MIN_RELATIVE_GAP - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Default value for the minimum relative gap threshold.
DEFAULT_NLAGS - Static variable in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
the default number of lags
DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
the default penalty function factory
DEFAULT_QA - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default acceptance parameter
DEFAULT_QV - Static variable in class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
the default visiting parameter
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_SAFETY_FACTOR - Static variable in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
Default value for the safety factor γ.
DEFAULT_SIGMA - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
the default value of the centering parameter
DEFAULT_STABLE_ITERATION_COUNT - Static variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
DEFAULT_THRESHOLD - Static variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
The default tolerance parameter tol.
DEFAULT_TOLERANCE - Static variable in class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
default tolerance
DEFAULT_TOLERANCE - Static variable in class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
default tolerance
DEFAULT_TOLERANCE - Static variable in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
 
DefaultDeflationCriterion - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
The default deflation criterion is to use eq.
DefaultDeflationCriterion() - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion.
DefaultDeflationCriterion(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion.
DefaultDeflationCriterion(double, DefaultDeflationCriterion.MatrixNorm) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion, with the algorithm for computing matrix norm for the matrix argument in isNegligible().
DefaultDeflationCriterion(DefaultDeflationCriterion.MatrixNorm) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Constructs the default deflation criterion, with the algorithm for computing matrix norm for the matrix argument in isNegligible().
DefaultDeflationCriterion.MatrixNorm - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Computes the norm of a given matrix.
DefaultMatrixStorage - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype
There are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose.
DefaultMatrixStorage(MatrixAccess) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Construct a DefaultMatrixStorage to wrap a storage for access.
DefaultRoot() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
 
DefaultSimplex - Class in dev.nm.solver.multivariate.initialization
A simplex optimization algorithm, e.g., Nelder-Mead, requires an initial simplex to start the search.
DefaultSimplex() - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex
Construct a simplex builder.
DefaultSimplex(double) - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex
Construct a simplex builder.
definingVector() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
definingVector() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Get the Householder defining vector which is orthogonal to the Householder hyperplane.
Deflation - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.
deflationCriterion - Variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
 
DeflationCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Determines whether a sub-diagonal entry is sufficiently small to be neglected.
degree() - Method in class dev.nm.analysis.function.polynomial.Polynomial
Get the degree of this polynomial.
deleteCol(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Deletes column i.
DELETED - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the deleted rows and columns
deleteRow(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Deletes row i.
delta - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
delta() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
Gets the shrinkage parameter δ.
deltaX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Return the distance between two adjacent points along the x-axis.
deltaX(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
Get the distance between two adjacent points along the axis with the given index.
deltaY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Return the distance between two adjacent points along the y-axis.
DenseData - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
This implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D array.
DenseData(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Construct a storage.
DenseData(double[], int, int, DoubleArrayOperation) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Construct a storage, and specify the implementations of the element-wise operations.
DenseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
This class implements the standard, dense, double based matrix representation.
DenseMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a matrix from a 2D double[][] array.
DenseMatrix(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a matrix from a 1D double[].
DenseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a 0 matrix of dimension nRows * nCols.
DenseMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Converts any matrix to the standard matrix representation.
DenseMatrix(DenseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Copy constructor performing a deep copy.
DenseMatrix(DenseMatrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
This constructor is useful for subclass to pass in computed value.
DenseMatrix(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Constructs a column matrix from a vector.
DenseMatrixMultiplication - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
Matrix operation that multiplies two matrices.
DenseMatrixMultiplicationByBlock - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
 
DenseMatrixMultiplicationByBlock() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
 
DenseMatrixMultiplicationByBlock(DenseMatrixMultiplicationByBlock.BlockAlgorithm) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
 
DenseMatrixMultiplicationByBlock.BlockAlgorithm - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
 
DenseMatrixMultiplicationByIjk - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
Implements the naive IJK algorithm.
DenseMatrixMultiplicationByIjk() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
 
DenseVector - Class in dev.nm.algebra.linear.vector.doubles.dense
This class implements the standard, dense, double based vector representation.
DenseVector(double...) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a double[].
DenseVector(int) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector.
DenseVector(int[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a int[].
DenseVector(int, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by repeating a value.
DenseVector(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector from a column or row matrix.
DenseVector(DenseVector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Copy constructor.
DenseVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Casts any vector to a DenseVector.
DenseVector(Double[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a Double[].
DenseVector(Collection<? extends Number>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a collection, with order defined by its iterator.
DenseVector(List<Double>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Constructs a vector, initialized by a List<Double>.
Densifiable - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.
density(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
This is the probability mass function.
density(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
This is the probability mass function for the discrete sample.
density(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
density(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
The density function, which, if exists, is the derivative of F.
density(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
density(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
density(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
The density function, which, if exists, is the derivative of F.
density(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
The probability density function \[ f(x; \mu,\sigma,\xi) = \begin{cases} \frac{1}{\sigma}\left(1+ \frac{\xi (x-\mu)}{\sigma}\right)^{\left(-\frac{1}{\xi} - 1\right)} & \text{for} \; \xi \neq 0, \\ \frac{1}{\sigma}\exp \left(-\frac{x-\mu}{\sigma}\right) & \text{for} \; \xi = 0 \end{cases} \] for \(x \ge \mu\) when \(\xi \ge 0\), and \(\mu \le x \le \mu - \sigma /\xi\) when \(\xi <0\).
density(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
The probability density function.
density(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
The probability density function.
density(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
density(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
density(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
density(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
density(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
density(double, double) - Method in interface dev.nm.stat.distribution.multivariate.BivariateProbabilityDistribution
The joint distribution density \(f_{X_1,X_2}(x_1,x_2)\).
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
density(double, double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
density(int, double) - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
Gets the (conditional) probability mass of making an observation in a particular state.
density(int, double) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
Gets the (conditional) probability density/mass of making an observation in a particular state.
density(int, double) - Method in class dev.nm.stat.hmm.mixture.MixtureHMM
 
density(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
 
density(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
density(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
The density function, which, if exists, is the derivative of F.
density(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
DEOptim - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
Differential Evolution (DE) is a global optimization method.
DEOptim(double, double, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim(double, double, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim(DEOptim.NewCellFactory, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
Construct a DEOptim to solve unconstrained minimization problems.
DEOptim.NewCellFactory - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
This factory constructs a new DEOptimCellFactory for each minimization problem.
DEOptim.Solution - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
This is the solution to a minimization problem using DEOptim.
DeOptimCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
 
DEOptimCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
A DEOptimCellFactory produces DEOptimCellFactory.DeOptimCells.
DEOptimCellFactory(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Construct an instance of a DEOptimCellFactory.
DEOptimCellFactory(DEOptimCellFactory) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Copy constructor.
DEOptimCellFactory.DeOptimCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
A DeOptimCell is a chromosome for a real valued function (an optimization problem) and a candidate solution.
dependence(double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD
The dependence function \(A\) for the parametric bivariate extreme value model.
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
dependence(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
depth() - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
Gets the depth of this node.
depth(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
depth(V) - Method in interface dev.nm.graph.RootedTree
Gets the (unweighted) distance of a vertex from the root of the vertex.
depth(V) - Method in class dev.nm.graph.type.SparseTree
 
DeRand1BinCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
 
DErf - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of the Error function, Erf.
DErf() - Constructor for class dev.nm.analysis.differentiation.univariate.DErf
 
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.link.LinkFunction
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
 
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
 
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
 
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
 
derivative(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
Derivative of the link function, i.e., g'(x).
DerivativeFunction - Interface in dev.nm.analysis.differentialequation.ode.ivp.problem
Defines the derivative function F(x, y) for ODE problems.
deRowMean(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the de-mean (row means) matrix of a given matrix.
det() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
The determinant of a Hilbert matrix is the reciprocal of an integer.
det(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the determinant of a matrix.
dev.nm.algebra.linear.matrix - package dev.nm.algebra.linear.matrix
 
dev.nm.algebra.linear.matrix.doubles - package dev.nm.algebra.linear.matrix.doubles
 
dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization - package dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
 
dev.nm.algebra.linear.matrix.doubles.factorization.eigen - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen
 
dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
 
dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
 
dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
 
dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr - package dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
 
dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination - package dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
 
dev.nm.algebra.linear.matrix.doubles.factorization.qr - package dev.nm.algebra.linear.matrix.doubles.factorization.qr
 
dev.nm.algebra.linear.matrix.doubles.factorization.svd - package dev.nm.algebra.linear.matrix.doubles.factorization.svd
 
dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3 - package dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3
 
dev.nm.algebra.linear.matrix.doubles.factorization.triangle - package dev.nm.algebra.linear.matrix.doubles.factorization.triangle
 
dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky - package dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
 
dev.nm.algebra.linear.matrix.doubles.linearsystem - package dev.nm.algebra.linear.matrix.doubles.linearsystem
 
dev.nm.algebra.linear.matrix.doubles.matrixtype - package dev.nm.algebra.linear.matrix.doubles.matrixtype
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.dense - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle - package dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation - package dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication - package dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
 
dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
 
dev.nm.algebra.linear.matrix.doubles.operation - package dev.nm.algebra.linear.matrix.doubles.operation
 
dev.nm.algebra.linear.matrix.doubles.operation.householder - package dev.nm.algebra.linear.matrix.doubles.operation.householder
 
dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite - package dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
 
dev.nm.algebra.linear.matrix.generic - package dev.nm.algebra.linear.matrix.generic
 
dev.nm.algebra.linear.matrix.generic.matrixtype - package dev.nm.algebra.linear.matrix.generic.matrixtype
 
dev.nm.algebra.linear.vector - package dev.nm.algebra.linear.vector
 
dev.nm.algebra.linear.vector.doubles - package dev.nm.algebra.linear.vector.doubles
 
dev.nm.algebra.linear.vector.doubles.dense - package dev.nm.algebra.linear.vector.doubles.dense
 
dev.nm.algebra.linear.vector.doubles.operation - package dev.nm.algebra.linear.vector.doubles.operation
 
dev.nm.algebra.structure - package dev.nm.algebra.structure
 
dev.nm.analysis.curvefit - package dev.nm.analysis.curvefit
 
dev.nm.analysis.curvefit.interpolation - package dev.nm.analysis.curvefit.interpolation
 
dev.nm.analysis.curvefit.interpolation.bivariate - package dev.nm.analysis.curvefit.interpolation.bivariate
 
dev.nm.analysis.curvefit.interpolation.multivariate - package dev.nm.analysis.curvefit.interpolation.multivariate
 
dev.nm.analysis.curvefit.interpolation.univariate - package dev.nm.analysis.curvefit.interpolation.univariate
 
dev.nm.analysis.differentialequation - package dev.nm.analysis.differentialequation
 
dev.nm.analysis.differentialequation.ode.ivp.problem - package dev.nm.analysis.differentialequation.ode.ivp.problem
 
dev.nm.analysis.differentialequation.ode.ivp.solver - package dev.nm.analysis.differentialequation.ode.ivp.solver
 
dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation - package dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation
 
dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton - package dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
 
dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta - package dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
 
dev.nm.analysis.differentialequation.pde - package dev.nm.analysis.differentialequation.pde
 
dev.nm.analysis.differentialequation.pde.finitedifference - package dev.nm.analysis.differentialequation.pde.finitedifference
 
dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2
 
dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 - package dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
 
dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
 
dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
 
dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
 
dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2 - package dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
 
dev.nm.analysis.differentiation - package dev.nm.analysis.differentiation
 
dev.nm.analysis.differentiation.differentiability - package dev.nm.analysis.differentiation.differentiability
 
dev.nm.analysis.differentiation.multivariate - package dev.nm.analysis.differentiation.multivariate
 
dev.nm.analysis.differentiation.univariate - package dev.nm.analysis.differentiation.univariate
 
dev.nm.analysis.function - package dev.nm.analysis.function
 
dev.nm.analysis.function.matrix - package dev.nm.analysis.function.matrix
 
dev.nm.analysis.function.polynomial - package dev.nm.analysis.function.polynomial
 
dev.nm.analysis.function.polynomial.root - package dev.nm.analysis.function.polynomial.root
 
dev.nm.analysis.function.polynomial.root.jenkinstraub - package dev.nm.analysis.function.polynomial.root.jenkinstraub
 
dev.nm.analysis.function.rn2r1 - package dev.nm.analysis.function.rn2r1
 
dev.nm.analysis.function.rn2r1.univariate - package dev.nm.analysis.function.rn2r1.univariate
 
dev.nm.analysis.function.rn2rm - package dev.nm.analysis.function.rn2rm
 
dev.nm.analysis.function.special - package dev.nm.analysis.function.special
 
dev.nm.analysis.function.special.beta - package dev.nm.analysis.function.special.beta
 
dev.nm.analysis.function.special.gamma - package dev.nm.analysis.function.special.gamma
 
dev.nm.analysis.function.special.gaussian - package dev.nm.analysis.function.special.gaussian
 
dev.nm.analysis.function.tuple - package dev.nm.analysis.function.tuple
 
dev.nm.analysis.integration.univariate - package dev.nm.analysis.integration.univariate
 
dev.nm.analysis.integration.univariate.riemann - package dev.nm.analysis.integration.univariate.riemann
 
dev.nm.analysis.integration.univariate.riemann.gaussian - package dev.nm.analysis.integration.univariate.riemann.gaussian
 
dev.nm.analysis.integration.univariate.riemann.gaussian.rule - package dev.nm.analysis.integration.univariate.riemann.gaussian.rule
 
dev.nm.analysis.integration.univariate.riemann.newtoncotes - package dev.nm.analysis.integration.univariate.riemann.newtoncotes
 
dev.nm.analysis.integration.univariate.riemann.substitution - package dev.nm.analysis.integration.univariate.riemann.substitution
 
dev.nm.analysis.root.multivariate - package dev.nm.analysis.root.multivariate
 
dev.nm.analysis.root.univariate - package dev.nm.analysis.root.univariate
 
dev.nm.analysis.sequence - package dev.nm.analysis.sequence
 
dev.nm.combinatorics - package dev.nm.combinatorics
 
dev.nm.dsp.univariate.operation.system.doubles - package dev.nm.dsp.univariate.operation.system.doubles
 
dev.nm.geometry - package dev.nm.geometry
 
dev.nm.geometry.polyline - package dev.nm.geometry.polyline
 
dev.nm.graph - package dev.nm.graph
 
dev.nm.graph.algorithm.shortestpath - package dev.nm.graph.algorithm.shortestpath
 
dev.nm.graph.algorithm.traversal - package dev.nm.graph.algorithm.traversal
 
dev.nm.graph.community - package dev.nm.graph.community
 
dev.nm.graph.type - package dev.nm.graph.type
 
dev.nm.interval - package dev.nm.interval
 
dev.nm.misc - package dev.nm.misc
 
dev.nm.misc.algorithm - package dev.nm.misc.algorithm
 
dev.nm.misc.algorithm.bb - package dev.nm.misc.algorithm.bb
 
dev.nm.misc.algorithm.iterative - package dev.nm.misc.algorithm.iterative
 
dev.nm.misc.algorithm.iterative.monitor - package dev.nm.misc.algorithm.iterative.monitor
 
dev.nm.misc.algorithm.iterative.tolerance - package dev.nm.misc.algorithm.iterative.tolerance
 
dev.nm.misc.algorithm.stopcondition - package dev.nm.misc.algorithm.stopcondition
 
dev.nm.misc.datastructure - package dev.nm.misc.datastructure
 
dev.nm.misc.datastructure.time - package dev.nm.misc.datastructure.time
 
dev.nm.misc.license - package dev.nm.misc.license
 
dev.nm.misc.parallel - package dev.nm.misc.parallel
 
dev.nm.number - package dev.nm.number
 
dev.nm.number.big - package dev.nm.number.big
 
dev.nm.number.complex - package dev.nm.number.complex
 
dev.nm.number.doublearray - package dev.nm.number.doublearray
 
dev.nm.root.univariate - package dev.nm.root.univariate
 
dev.nm.root.univariate.bracketsearch - package dev.nm.root.univariate.bracketsearch
 
dev.nm.solver - package dev.nm.solver
 
dev.nm.solver.multivariate.constrained - package dev.nm.solver.multivariate.constrained
 
dev.nm.solver.multivariate.constrained.constraint - package dev.nm.solver.multivariate.constrained.constraint
 
dev.nm.solver.multivariate.constrained.constraint.general - package dev.nm.solver.multivariate.constrained.constraint.general
 
dev.nm.solver.multivariate.constrained.constraint.linear - package dev.nm.solver.multivariate.constrained.constraint.linear
 
dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing - package dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
 
dev.nm.solver.multivariate.constrained.convex.sdp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.problem
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
 
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp - package dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
 
dev.nm.solver.multivariate.constrained.general.box - package dev.nm.solver.multivariate.constrained.general.box
 
dev.nm.solver.multivariate.constrained.general.penaltymethod - package dev.nm.solver.multivariate.constrained.general.penaltymethod
 
dev.nm.solver.multivariate.constrained.general.sqp.activeset - package dev.nm.solver.multivariate.constrained.general.sqp.activeset
 
dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint - package dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
 
dev.nm.solver.multivariate.constrained.integer - package dev.nm.solver.multivariate.constrained.integer
 
dev.nm.solver.multivariate.constrained.integer.bruteforce - package dev.nm.solver.multivariate.constrained.integer.bruteforce
 
dev.nm.solver.multivariate.constrained.integer.linear.bb - package dev.nm.solver.multivariate.constrained.integer.linear.bb
 
dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane - package dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
 
dev.nm.solver.multivariate.constrained.integer.linear.problem - package dev.nm.solver.multivariate.constrained.integer.linear.problem
 
dev.nm.solver.multivariate.constrained.problem - package dev.nm.solver.multivariate.constrained.problem
 
dev.nm.solver.multivariate.geneticalgorithm - package dev.nm.solver.multivariate.geneticalgorithm
 
dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
 
dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
 
dev.nm.solver.multivariate.geneticalgorithm.minimizer.local - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
 
dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
 
dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration - package dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
 
dev.nm.solver.multivariate.initialization - package dev.nm.solver.multivariate.initialization
 
dev.nm.solver.multivariate.minmax - package dev.nm.solver.multivariate.minmax
 
dev.nm.solver.multivariate.unconstrained - package dev.nm.solver.multivariate.unconstrained
 
dev.nm.solver.multivariate.unconstrained.annealing - package dev.nm.solver.multivariate.unconstrained.annealing
 
dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction - package dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
 
dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction - package dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
 
dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction - package dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
 
dev.nm.solver.multivariate.unconstrained.c2 - package dev.nm.solver.multivariate.unconstrained.c2
 
dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection - package dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
 
dev.nm.solver.multivariate.unconstrained.c2.linesearch - package dev.nm.solver.multivariate.unconstrained.c2.linesearch
 
dev.nm.solver.multivariate.unconstrained.c2.quasinewton - package dev.nm.solver.multivariate.unconstrained.c2.quasinewton
 
dev.nm.solver.multivariate.unconstrained.c2.steepestdescent - package dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
 
dev.nm.solver.problem - package dev.nm.solver.problem
 
dev.nm.stat.cointegration - package dev.nm.stat.cointegration
 
dev.nm.stat.covariance - package dev.nm.stat.covariance
 
dev.nm.stat.covariance.covarianceselection - package dev.nm.stat.covariance.covarianceselection
 
dev.nm.stat.covariance.covarianceselection.lasso - package dev.nm.stat.covariance.covarianceselection.lasso
 
dev.nm.stat.covariance.nlshrink - package dev.nm.stat.covariance.nlshrink
 
dev.nm.stat.covariance.nlshrink.quest - package dev.nm.stat.covariance.nlshrink.quest
 
dev.nm.stat.descriptive - package dev.nm.stat.descriptive
 
dev.nm.stat.descriptive.correlation - package dev.nm.stat.descriptive.correlation
 
dev.nm.stat.descriptive.covariance - package dev.nm.stat.descriptive.covariance
 
dev.nm.stat.descriptive.moment - package dev.nm.stat.descriptive.moment
 
dev.nm.stat.descriptive.moment.weighted - package dev.nm.stat.descriptive.moment.weighted
 
dev.nm.stat.descriptive.rank - package dev.nm.stat.descriptive.rank
 
dev.nm.stat.distribution.discrete - package dev.nm.stat.distribution.discrete
 
dev.nm.stat.distribution.multivariate - package dev.nm.stat.distribution.multivariate
 
dev.nm.stat.distribution.multivariate.exponentialfamily - package dev.nm.stat.distribution.multivariate.exponentialfamily
 
dev.nm.stat.distribution.univariate - package dev.nm.stat.distribution.univariate
 
dev.nm.stat.distribution.univariate.exponentialfamily - package dev.nm.stat.distribution.univariate.exponentialfamily
 
dev.nm.stat.dlm.multivariate - package dev.nm.stat.dlm.multivariate
 
dev.nm.stat.dlm.univariate - package dev.nm.stat.dlm.univariate
 
dev.nm.stat.evt.cluster - package dev.nm.stat.evt.cluster
 
dev.nm.stat.evt.evd.bivariate - package dev.nm.stat.evt.evd.bivariate
 
dev.nm.stat.evt.evd.univariate - package dev.nm.stat.evt.evd.univariate
 
dev.nm.stat.evt.evd.univariate.fitting - package dev.nm.stat.evt.evd.univariate.fitting
 
dev.nm.stat.evt.evd.univariate.fitting.acer - package dev.nm.stat.evt.evd.univariate.fitting.acer
 
dev.nm.stat.evt.evd.univariate.fitting.acer.empirical - package dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
 
dev.nm.stat.evt.evd.univariate.fitting.pot - package dev.nm.stat.evt.evd.univariate.fitting.pot
 
dev.nm.stat.evt.evd.univariate.rng - package dev.nm.stat.evt.evd.univariate.rng
 
dev.nm.stat.evt.exi - package dev.nm.stat.evt.exi
 
dev.nm.stat.evt.function - package dev.nm.stat.evt.function
 
dev.nm.stat.evt.markovchain - package dev.nm.stat.evt.markovchain
 
dev.nm.stat.evt.timeseries - package dev.nm.stat.evt.timeseries
 
dev.nm.stat.factor.factoranalysis - package dev.nm.stat.factor.factoranalysis
 
dev.nm.stat.factor.implicitmodelpca - package dev.nm.stat.factor.implicitmodelpca
 
dev.nm.stat.factor.pca - package dev.nm.stat.factor.pca
 
dev.nm.stat.hmm - package dev.nm.stat.hmm
 
dev.nm.stat.hmm.discrete - package dev.nm.stat.hmm.discrete
 
dev.nm.stat.hmm.mixture - package dev.nm.stat.hmm.mixture
 
dev.nm.stat.hmm.mixture.distribution - package dev.nm.stat.hmm.mixture.distribution
 
dev.nm.stat.markovchain - package dev.nm.stat.markovchain
 
dev.nm.stat.random - package dev.nm.stat.random
 
dev.nm.stat.random.rng - package dev.nm.stat.random.rng
 
dev.nm.stat.random.rng.concurrent.cache - package dev.nm.stat.random.rng.concurrent.cache
 
dev.nm.stat.random.rng.concurrent.context - package dev.nm.stat.random.rng.concurrent.context
 
dev.nm.stat.random.rng.multivariate - package dev.nm.stat.random.rng.multivariate
 
dev.nm.stat.random.rng.multivariate.mcmc.hybrid - package dev.nm.stat.random.rng.multivariate.mcmc.hybrid
 
dev.nm.stat.random.rng.multivariate.mcmc.metropolis - package dev.nm.stat.random.rng.multivariate.mcmc.metropolis
 
dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction - package dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
 
dev.nm.stat.random.rng.univariate - package dev.nm.stat.random.rng.univariate
 
dev.nm.stat.random.rng.univariate.beta - package dev.nm.stat.random.rng.univariate.beta
 
dev.nm.stat.random.rng.univariate.exp - package dev.nm.stat.random.rng.univariate.exp
 
dev.nm.stat.random.rng.univariate.gamma - package dev.nm.stat.random.rng.univariate.gamma
 
dev.nm.stat.random.rng.univariate.normal - package dev.nm.stat.random.rng.univariate.normal
 
dev.nm.stat.random.rng.univariate.normal.truncated - package dev.nm.stat.random.rng.univariate.normal.truncated
 
dev.nm.stat.random.rng.univariate.poisson - package dev.nm.stat.random.rng.univariate.poisson
 
dev.nm.stat.random.rng.univariate.uniform - package dev.nm.stat.random.rng.univariate.uniform
 
dev.nm.stat.random.rng.univariate.uniform.linear - package dev.nm.stat.random.rng.univariate.uniform.linear
 
dev.nm.stat.random.rng.univariate.uniform.mersennetwister - package dev.nm.stat.random.rng.univariate.uniform.mersennetwister
 
dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation - package dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
 
dev.nm.stat.random.sampler.resampler - package dev.nm.stat.random.sampler.resampler
 
dev.nm.stat.random.sampler.resampler.bootstrap - package dev.nm.stat.random.sampler.resampler.bootstrap
 
dev.nm.stat.random.sampler.resampler.bootstrap.block - package dev.nm.stat.random.sampler.resampler.bootstrap.block
 
dev.nm.stat.random.sampler.resampler.multivariate - package dev.nm.stat.random.sampler.resampler.multivariate
 
dev.nm.stat.random.variancereduction - package dev.nm.stat.random.variancereduction
 
dev.nm.stat.regression - package dev.nm.stat.regression
 
dev.nm.stat.regression.linear - package dev.nm.stat.regression.linear
 
dev.nm.stat.regression.linear.glm - package dev.nm.stat.regression.linear.glm
 
dev.nm.stat.regression.linear.glm.distribution - package dev.nm.stat.regression.linear.glm.distribution
 
dev.nm.stat.regression.linear.glm.distribution.link - package dev.nm.stat.regression.linear.glm.distribution.link
 
dev.nm.stat.regression.linear.glm.modelselection - package dev.nm.stat.regression.linear.glm.modelselection
 
dev.nm.stat.regression.linear.glm.quasi - package dev.nm.stat.regression.linear.glm.quasi
 
dev.nm.stat.regression.linear.glm.quasi.family - package dev.nm.stat.regression.linear.glm.quasi.family
 
dev.nm.stat.regression.linear.lasso - package dev.nm.stat.regression.linear.lasso
 
dev.nm.stat.regression.linear.lasso.lars - package dev.nm.stat.regression.linear.lasso.lars
 
dev.nm.stat.regression.linear.logistic - package dev.nm.stat.regression.linear.logistic
 
dev.nm.stat.regression.linear.ols - package dev.nm.stat.regression.linear.ols
 
dev.nm.stat.regression.linear.panel - package dev.nm.stat.regression.linear.panel
 
dev.nm.stat.regression.linear.residualanalysis - package dev.nm.stat.regression.linear.residualanalysis
 
dev.nm.stat.stochasticprocess.multivariate.random - package dev.nm.stat.stochasticprocess.multivariate.random
 
dev.nm.stat.stochasticprocess.multivariate.sde - package dev.nm.stat.stochasticprocess.multivariate.sde
 
dev.nm.stat.stochasticprocess.multivariate.sde.coefficients - package dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
 
dev.nm.stat.stochasticprocess.multivariate.sde.discrete - package dev.nm.stat.stochasticprocess.multivariate.sde.discrete
 
dev.nm.stat.stochasticprocess.timegrid - package dev.nm.stat.stochasticprocess.timegrid
 
dev.nm.stat.stochasticprocess.univariate.filtration - package dev.nm.stat.stochasticprocess.univariate.filtration
 
dev.nm.stat.stochasticprocess.univariate.integration - package dev.nm.stat.stochasticprocess.univariate.integration
 
dev.nm.stat.stochasticprocess.univariate.random - package dev.nm.stat.stochasticprocess.univariate.random
 
dev.nm.stat.stochasticprocess.univariate.sde - package dev.nm.stat.stochasticprocess.univariate.sde
 
dev.nm.stat.stochasticprocess.univariate.sde.coefficients - package dev.nm.stat.stochasticprocess.univariate.sde.coefficients
 
dev.nm.stat.stochasticprocess.univariate.sde.discrete - package dev.nm.stat.stochasticprocess.univariate.sde.discrete
 
dev.nm.stat.stochasticprocess.univariate.sde.process - package dev.nm.stat.stochasticprocess.univariate.sde.process
 
dev.nm.stat.stochasticprocess.univariate.sde.process.ou - package dev.nm.stat.stochasticprocess.univariate.sde.process.ou
 
dev.nm.stat.test - package dev.nm.stat.test
 
dev.nm.stat.test.distribution - package dev.nm.stat.test.distribution
 
dev.nm.stat.test.distribution.kolmogorov - package dev.nm.stat.test.distribution.kolmogorov
 
dev.nm.stat.test.distribution.normality - package dev.nm.stat.test.distribution.normality
 
dev.nm.stat.test.distribution.pearson - package dev.nm.stat.test.distribution.pearson
 
dev.nm.stat.test.mean - package dev.nm.stat.test.mean
 
dev.nm.stat.test.rank - package dev.nm.stat.test.rank
 
dev.nm.stat.test.rank.wilcoxon - package dev.nm.stat.test.rank.wilcoxon
 
dev.nm.stat.test.regression.linear.heteroskedasticity - package dev.nm.stat.test.regression.linear.heteroskedasticity
 
dev.nm.stat.test.timeseries.adf - package dev.nm.stat.test.timeseries.adf
 
dev.nm.stat.test.timeseries.adf.table - package dev.nm.stat.test.timeseries.adf.table
 
dev.nm.stat.test.timeseries.portmanteau - package dev.nm.stat.test.timeseries.portmanteau
 
dev.nm.stat.test.variance - package dev.nm.stat.test.variance
 
dev.nm.stat.timeseries.datastructure - package dev.nm.stat.timeseries.datastructure
 
dev.nm.stat.timeseries.datastructure.multivariate - package dev.nm.stat.timeseries.datastructure.multivariate
 
dev.nm.stat.timeseries.datastructure.multivariate.realtime - package dev.nm.stat.timeseries.datastructure.multivariate.realtime
 
dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime - package dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
 
dev.nm.stat.timeseries.datastructure.univariate - package dev.nm.stat.timeseries.datastructure.univariate
 
dev.nm.stat.timeseries.datastructure.univariate.realtime - package dev.nm.stat.timeseries.datastructure.univariate.realtime
 
dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime - package dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
 
dev.nm.stat.timeseries.linear.multivariate - package dev.nm.stat.timeseries.linear.multivariate
 
dev.nm.stat.timeseries.linear.multivariate.arima - package dev.nm.stat.timeseries.linear.multivariate.arima
 
dev.nm.stat.timeseries.linear.multivariate.stationaryprocess - package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess
 
dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma - package dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
 
dev.nm.stat.timeseries.linear.univariate - package dev.nm.stat.timeseries.linear.univariate
 
dev.nm.stat.timeseries.linear.univariate.arima - package dev.nm.stat.timeseries.linear.univariate.arima
 
dev.nm.stat.timeseries.linear.univariate.sample - package dev.nm.stat.timeseries.linear.univariate.sample
 
dev.nm.stat.timeseries.linear.univariate.stationaryprocess - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess
 
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
 
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch
 
dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch - package dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
 
deviance() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
Gets the (total) deviance.
deviance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
Gets the residual deviance.
deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
deviance(double, double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
Deviance D(y;μ^) measures the goodness-of-fit of a model, which is defined as the difference between the maximum log likelihood achievable and that achieved by the model.
deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
deviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
devianceResiduals() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
Gets the deviances residuals.
devianceResiduals() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
Gets the residuals, ε.
deviances() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
Gets the deviances of the observations.
deviances() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMResiduals
 
df() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the degree of freedom.
df() - Method in class dev.nm.stat.test.mean.T
Get the degree of freedom.
df(double, double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
Compute the finite difference for f at x with an increment h for the n-th order using either forward, backward, or central difference.
df1() - Method in class dev.nm.stat.test.mean.OneWayANOVA
Get the first degree of freedom.
df2() - Method in class dev.nm.stat.test.mean.OneWayANOVA
Get the second degree of freedom.
Dfdx - Class in dev.nm.analysis.differentiation.univariate
The first derivative is a measure of how a function changes as its input changes.
Dfdx(UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentiation.univariate.Dfdx
Construct, using the central finite difference, the first order derivative function of a univariate function f.
Dfdx(UnivariateRealFunction, Dfdx.Method) - Constructor for class dev.nm.analysis.differentiation.univariate.Dfdx
Construct the first order derivative function of a univariate function f.
Dfdx.Method - Enum in dev.nm.analysis.differentiation.univariate
the available methods to compute the numerical derivative
DFFITS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
DFFITS, Welsch and Kuh Measure.
DFPMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
DFPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
Construct a multivariate minimizer using the DFP method.
DFS<V> - Class in dev.nm.graph.algorithm.traversal
This class implements the depth-first-search using iteration.
DFS(Graph<? extends V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.DFS
Constructs a DFS tree of a graph.
DFS(Graph<W, ? extends Edge<V>>, V, int) - Static method in class dev.nm.graph.algorithm.traversal.DFS
Runs the depth-first-search on a graph from a designated root.
DFS.Node<V> - Class in dev.nm.graph.algorithm.traversal
This is a node in a DFS-spanning tree.
DFS.Node.Color - Enum in dev.nm.graph.algorithm.traversal
This is the coloring scheme of visits.
DGamma - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of the Gamma function, \({d \mathrm{\Gamma}(x) \over dx}\).
DGamma() - Constructor for class dev.nm.analysis.differentiation.univariate.DGamma
 
DGaussian - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of a Gaussian function, \({d \mathrm{\phi}(x) \over dx}\).
DGaussian(Gaussian) - Constructor for class dev.nm.analysis.differentiation.univariate.DGaussian
Construct the derivative function of a Gaussian function.
Dhat() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Gets the modified diagonal matrix which is positive definite.
di2UnDiGraph(DiGraph<V, ? extends Arc<V>>) - Static method in class dev.nm.graph.GraphUtils
Converts a directed graph into an undirected graph by removing the direction of all arcs.
diagnostics() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
Gets the diagnostic measures of an OLS regression.
diagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the diagonal of a matrix as a vector.
diagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the diagonal of a sparse matrix as a sparse vector.
Diagonalization() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
 
diagonalMatrix(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Gets the diagonal of a matrix.
DiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
A diagonal matrix has non-zero entries only on the main diagonal.
DiagonalMatrix(double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Constructs a diagonal matrix from a double[].
DiagonalMatrix(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
DiagonalMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Constructs a 0 diagonal matrix of dimension dim * dim.
DiagonalMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
DiagonalMatrix(DiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Copy constructor.
DiagonalSum - Class in dev.nm.algebra.linear.matrix.doubles.operation
Add diagonal elements to a matrix, an efficient implementation.
DiagonalSum(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
DiagonalSum(Matrix, double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
DiagonalSum(Matrix, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
DICKEY_FULLER - dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
Deprecated.
the original version of the Dickey-Fuller test, developed in 1979
diff(double[]) - Static method in class dev.nm.number.DoubleUtils
Gets the first differences of an array.
diff(double[][]) - Static method in class dev.nm.number.DoubleUtils
Gets the first differences of an array of vectors.
diff(double[][], int, int) - Static method in class dev.nm.number.DoubleUtils
Gets the lagged and iterated differences of vectors.
diff(double[], int, int) - Static method in class dev.nm.number.DoubleUtils
Gets the lagged and iterated differences.
diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct an instance of MultivariateGenericTimeTimeSeries by taking the first difference d times.
diff(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries by taking the first difference d times.
diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Construct an instance of GenericTimeTimeSeries by taking the first difference d times.
diff(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
Constructs an instance of SimpleTimeSeries by taking the first difference d times.
DifferencedIntTimeTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
Differencing of a time series xt in discrete time t is the transformation of the series to a new time series (1-B)xt where the new values are the differences between consecutive values of xt.
DifferencedIntTimeTimeSeries(IntTimeTimeSeries, int) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
 
diffusion() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
Get the diffusion coefficient: \(\sigma(t, X_t, Z_t, ...)\).
Diffusion - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients
This class represents the diffusion term, σ, of a univariate SDE.
DiffusionMatrix - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
The diffusion term, σ, of an SDE takes this form: σ(dt, Xt, Zt, ...).
DiffusionSigma - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
This class implements the diffusion term in the form of a diffusion matrix.
DiffusionSigma() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
 
Digamma - Class in dev.nm.analysis.function.special.gamma
The digamma function is defined as the logarithmic derivative of the gamma function.
Digamma() - Constructor for class dev.nm.analysis.function.special.gamma.Digamma
 
DiGraph<V,​E extends Arc<V>> - Interface in dev.nm.graph
A directed graph or digraph is a graph, or set of nodes connected by edges, where the edges have a direction associated with them.
Dijkstra<V> - Class in dev.nm.graph.algorithm.shortestpath
Dijkstra's algorithm is a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge path costs, producing a shortest path tree.
Dijkstra(DiGraph<V, ? extends WeightedArc<V>>, V) - Constructor for class dev.nm.graph.algorithm.shortestpath.Dijkstra
 
dim - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
the variable dimension
dim() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the dimension of the process.
dimension() - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
 
dimension() - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
Get the total number of dimensions of the grid.
dimension() - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
 
dimension() - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
Gets the dimension of y.
dimension() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Gets the dimension of y.
dimension() - Method in class dev.nm.geometry.LineSegment
Get the dimension of the coordinate space.
dimension() - Method in class dev.nm.geometry.Point
Get the dimension of the coordinate space.
dimension() - Method in interface dev.nm.geometry.polyline.PolygonalChain
Get the number of dimensions of this polygonal chain.
dimension() - Method in class dev.nm.geometry.polyline.PolygonalChainByArray
 
dimension() - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
 
dimension() - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
Returns the number of dimensions of the collection.
dimension() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
Get the number of variables.
dimension() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.EqualityConstraints
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
 
dimension() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
 
dimension() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
 
dimension() - Method in interface dev.nm.solver.problem.OptimProblem
Get the number of variables.
dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the dimension of the system, i.e., the dimension of the state vector.
dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Gets the dimension of observation yt.
dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Gets the dimension of state xt.
dimension() - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Get the dimension of observation yt.
dimension() - Method in class dev.nm.stat.dlm.univariate.StateEquation
Get the dimension of state xt.
dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
 
dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated.
 
dimension() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
Get the dimension of the process.
dimension() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Get the dimension of the process.
dimension() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
dimension() - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries
Get the dimension of the multivariate time series.
dimension() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
dimension() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the dimension of multivariate time series.
dimension() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the dimension of the multivariate time series.
DimensionCheck - Class in dev.nm.misc.datastructure
These are the utility functions for checking table dimension.
dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
 
dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
 
dimensionOfDomain() - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
 
dimensionOfDomain() - Method in interface dev.nm.analysis.function.Function
Get the number of variables the function has.
dimensionOfDomain() - Method in class dev.nm.analysis.function.matrix.R1toMatrix
 
dimensionOfDomain() - Method in class dev.nm.analysis.function.matrix.R2toMatrix
 
dimensionOfDomain() - Method in class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
 
dimensionOfDomain() - Method in class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
 
dimensionOfDomain() - Method in class dev.nm.analysis.function.SubFunction
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Gets the dimension of the original (unstacked) problem.
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Gets the dimension of the original (unstacked) problem.
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
 
dimensionOfDomain() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
 
dimensionOfDomain() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
 
dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
 
dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
 
dimensionOfRange() - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
 
dimensionOfRange() - Method in interface dev.nm.analysis.function.Function
Get the dimension of the range space of the function.
dimensionOfRange() - Method in class dev.nm.analysis.function.matrix.R1toMatrix
 
dimensionOfRange() - Method in class dev.nm.analysis.function.matrix.R2toMatrix
 
dimensionOfRange() - Method in class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
 
dimensionOfRange() - Method in class dev.nm.analysis.function.rn2rm.AbstractRealVectorFunction
 
dimensionOfRange() - Method in class dev.nm.analysis.function.SubFunction
 
dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
 
dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
 
dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
 
dimensionOfRange() - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyFunction
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
 
dimensionOfRange() - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
 
DirichletDistribution - Class in dev.nm.stat.distribution.multivariate
The Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted Dir(a), is a family of continuous multivariate probability distributions parametrized by a vector a of positive reals.
DirichletDistribution(double[]) - Constructor for class dev.nm.stat.distribution.multivariate.DirichletDistribution
Constructs an instance of Dirichlet distribution.
DirichletDistribution(double[], double) - Constructor for class dev.nm.stat.distribution.multivariate.DirichletDistribution
Constructs an instance of Dirichlet distribution.
dis_m_LF_list - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
whole m_LF_zxi_list(including the modified Stieltjes transform of endpoints)
DiscreteHMM - Class in dev.nm.stat.hmm.discrete
This is the discrete hidden Markov model as defined by Rabiner.
DiscreteHMM(Vector, Matrix, Matrix) - Constructor for class dev.nm.stat.hmm.discrete.DiscreteHMM
Constructs a discrete hidden Markov model.
DiscreteHMM(DiscreteHMM) - Constructor for class dev.nm.stat.hmm.discrete.DiscreteHMM
Copy constructor.
DiscreteSDE - Interface in dev.nm.stat.stochasticprocess.univariate.sde.discrete
This interface represents the discrete approximation of a univariate SDE.
discretization - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
Discretization(double, double, double) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid.Discretization
Constructs a discretization of an interval.
dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
dispersion(Vector, Vector, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
Different distribution models have different ways to compute dispersion, Φ.
dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
dispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
dist - Variable in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
 
distance(Point) - Method in class dev.nm.geometry.LineSegment
Calculate the shortest distance between a point and this line segment in Euclidean geometry.
distance(Point) - Method in class dev.nm.geometry.Point
Compute the Euclidean distance between this point and the given point.
distance(V) - Method in class dev.nm.graph.algorithm.shortestpath.Dijkstra
 
distance(V) - Method in interface dev.nm.graph.algorithm.shortestpath.ShortestPath
Gets the shortest distance from the source to a vertex.
distribution() - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
 
distribution() - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
 
DiversificationMeasure - Interface in tech.nmfin.portfoliooptimization.corvalan2005.diversification
Defines the diversification of a portfolio.
divide(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
 
divide(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
Divide one double array by another, entry-by-entry.
divide(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
divide(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
divide(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
divide(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
divide(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
divide(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
divide(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Divide this by that, entry-by-entry.
divide(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
A vector is divided by another vector, element-by-element.
divide(Complex) - Method in class dev.nm.number.complex.Complex
Compute the quotient of this complex number divided by another complex number.
divide(Real) - Method in class dev.nm.number.Real
 
divide(Real, int) - Method in class dev.nm.number.Real
/ : R × R → R
divide(F) - Method in interface dev.nm.algebra.structure.Field
/ : F × F → F
DividedDifferences - Class in dev.nm.analysis.curvefit.interpolation.univariate
Divided differences is recursive division process for calculating the coefficients in the interpolation polynomial in the Newton form.
DividedDifferences(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
Construct divided differences from a given collection of ordered pairs.
DividedDifferences(SortedOrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
Construct divided differences from a given sorted collection of ordered pairs.
dk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
the line search direction at the k-th iteration
DLM - Class in dev.nm.stat.dlm.univariate
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
DLM(double, double, ObservationEquation, StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.DLM
Construct a univariate controlled dynamic linear model.
DLM(DLM) - Constructor for class dev.nm.stat.dlm.univariate.DLM
Copy constructor.
DLMSeries - Class in dev.nm.stat.dlm.univariate
This is a simulator for a multivariate controlled dynamic linear model process.
DLMSeries(int, DLM) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
Simulate a univariate controlled dynamic linear model process.
DLMSeries(int, DLM, double[]) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
Simulate a univariate controlled dynamic linear model process.
DLMSeries(int, DLM, double[], RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.DLMSeries
Simulate a univariate controlled dynamic linear model process.
DLMSeries.Entry - Class in dev.nm.stat.dlm.univariate
This is the TimeSeries.Entry for a univariate DLM time series.
DLMSim - Class in dev.nm.stat.dlm.univariate
This is a simulator for a univariate controlled dynamic linear model process.
DLMSim(DLM, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.DLMSim
Simulate a univariate controlled dynamic linear model process.
DLMSim.Innovation - Class in dev.nm.stat.dlm.univariate
a simulated innovation
dof() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Gets the degree of freedom in the factor analysis model.
DOKSparseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys.
DOKSparseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
Construct a sparse matrix in DOK format.
DOKSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
Construct a sparse matrix in DOK format.
DOKSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
Construct a sparse matrix in DOK format by a list of non-zero entries.
DOKSparseMatrix(DOKSparseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
Copy constructor.
domain - Variable in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
the integer values the variable can take
Doolittle - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
Doolittle algorithm is a LU decomposition algorithm which decomposes a square matrix A into: P is an n x n permutation matrix; L is an n x n (unit) lower triangular matrix; U is an n x n upper triangular matrix, such that PA = LU.
Doolittle(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
Runs Doolittle algorithm on a square matrix for LU decomposition.
Doolittle(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
Runs Doolittle algorithm on a square matrix for LU decomposition.
Doolittle(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
Runs Doolittle algorithm on a square matrix for LU decomposition.
Doolittle(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
Runs Doolittle algorithm on a square matrix for LU decomposition.
dotProduct(double[], double[]) - Static method in class dev.nm.analysis.function.FunctionOps
\(x_1 \cdot x_2\)
dotProduct(long[], long[]) - Static method in class dev.nm.analysis.function.FunctionOps
\(x_1 \cdot x_2\)
doubleArray2intArray(double...) - Static method in class dev.nm.number.DoubleUtils
Convert a double array to an int array, rounding down if necessary.
doubleArray2List(double...) - Static method in class dev.nm.number.DoubleUtils
Convert a double array to a list.
DoubleArrayMath - Class in dev.nm.number.doublearray
These are the math functions that operate on double[].
DoubleArrayOperation - Interface in dev.nm.number.doublearray
It is possible to provide different implementations for different platforms, hardware, etc.
DoubleBruteForceMinimizer - Class in dev.nm.solver.multivariate.unconstrained
This implementation solves an unconstrained minimization problem by brute force search for all given possible values.
DoubleBruteForceMinimizer(boolean) - Constructor for class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer
 
DoubleExponential - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation speeds up the convergence of the Trapezoidal Rule exponentially.
DoubleExponential(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
Construct a DoubleExponential substitution rule by trying to automatically determine the substitution rule.
DoubleExponential4HalfRealLine - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation is good for the region \((0, +\infty)\).
DoubleExponential4HalfRealLine(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential4HalfRealLine
Construct a DoubleExponential4HalfRealLine substitution rule.
DoubleExponential4RealLine - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation is good for the region \((-\infty, +\infty)\).
DoubleExponential4RealLine(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential4RealLine
Construct a DoubleExponential4RealLine substitution rule.
doubleMod(double, double) - Static method in class dev.nm.number.DoubleUtils
Double modulo, analogous to x % y when x and y are ints.
DoubleUtils - Class in dev.nm.number
These are the utility functions to manipulate double and int.
DoubleUtils.ifelse - Interface in dev.nm.number
Return a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is true or false.
DoubleUtils.RoundingScheme - Enum in dev.nm.number
the available schemes to round a number
DoubleUtils.which - Interface in dev.nm.number
Decide whether x satisfies the boolean test.
doubleValue() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
Construct a DenseMatrix equivalent of this Complex matrix (rounded if necessary).
doubleValue() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
Construct a DenseMatrix equivalent of this Real matrix (rounded if necessary).
doubleValue() - Method in class dev.nm.number.complex.Complex
 
doubleValue() - Method in class dev.nm.number.Real
 
doubleValue() - Method in class dev.nm.number.ScientificNotation
 
DOWN - dev.nm.number.DoubleUtils.RoundingScheme
Always round down.
DOWN - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
 
DPolynomial - Class in dev.nm.analysis.differentiation.univariate
This is the first order derivative function of a Polynomial, which, again, is a polynomial.
DPolynomial(Polynomial) - Constructor for class dev.nm.analysis.differentiation.univariate.DPolynomial
Construct the derivative function of a Polynomial, which, again, is a polynomial.
DQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the qd-array Z to high relative accuracy.
DQDS(int, Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.DQDS
Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the q and e values to high relative accuracy.
dReturns() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
Gets the difference between predicted accumulated returns and realized accumulated returns at time T-H+1, the last historical fully realized accumulated return.
drift() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
Get the drift: \(\mu(t,X_t,Z_t,...)\).
Drift - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients
This class represents the drift term, μ, of a univariate SDE.
drift1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
drift2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
DriftVector - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
The drift term, μ, of an SDE takes this form: μ(dt, Xt, Zt, ...).
drop(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct an instance of MultivariateGenericTimeTimeSeries by dropping the leading nItems entries, those most backward in time entries.
drop(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries by dropping the leading nItems entries.
drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Construct an instance of GenericTimeTimeSeries by dropping the leading nItems entries.
drop(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
Constructs an instance of SimpleTimeSeries by dropping the leading nItems entries.
dropFactor(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Drops the indexed factor.
dt - Variable in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
 
dt - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
dt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the current time differential.
dt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get all the time increments.
dt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Get the current time differential.
dt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Get the time interval \(\delta_t\) of this generator.
dt(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the i-th time increment.
du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
Get an array of the measure values.
du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
 
du() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
 
dUdx(RealVectorFunction) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
Gets the derivative of the potential function, given the derivative of the log density.
DuplicatedAbscissae - Exception in dev.nm.analysis.function.tuple
This exception is thrown when a function has two same x-abscissae, hence ill-defined.
DuplicatedAbscissae() - Constructor for exception dev.nm.analysis.function.tuple.DuplicatedAbscissae
Construct an instance.
DuplicatedAbscissae(String) - Constructor for exception dev.nm.analysis.function.tuple.DuplicatedAbscissae
Construct a DuplicatedAbscissae runtime exception with an error message.
DURING - dev.nm.interval.IntervalRelation
X during Y.
DURING_INVERSE - dev.nm.interval.IntervalRelation
Y during X.
DVInv() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
Computes D / V(μ).
dWt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the increment of the driving Brownian motion during the time differential.
dWt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Get the increment of the driving Brownian motion during the time differential.
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
 
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
 
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
 
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
 
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
 
dx() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
 
dx() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
the first order derivative of the transformation: x'(t) = dx(t)/dt
dx(BivariateGrid, int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
Get the partial derivative \(\frac{\partial z}{\partial x}\), at the given position in the grid.
dx(BivariateGrid, int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
 
dxdy(BivariateGrid, int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
Get the cross derivative \(\frac{\partial^2 z}{\partial x \partial y}\), at the given position in the grid.
dxdy(BivariateGrid, int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
 
dXt(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
 
dXt(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
This is the SDE specification of a stochastic process.
dXt(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
This is the SDE specification of a stochastic process.
dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
 
dXt(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.discrete.DiscreteSDE
This is the SDE specification of a stochastic process.
dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
This is the SDE specification of a stochastic process.
dXt(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
This is the SDE specification of a stochastic process.
dy() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Gets the first order derivative function.
dy(BivariateGrid, int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation.PartialDerivatives
Get the partial derivative \(\frac{\partial z}{\partial y}\), at the given position in the grid.
dy(BivariateGrid, int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
 
DynamicCreator - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
Performs the Dynamic Creation algorithm (DC) to generate parameters for MersenneTwister.
DynamicCreator(MersenneExponent, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
Constructs a new instance which aims to generate MT RNGs with the given period.
DynamicCreatorException - Exception in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
Indicates that a problem has occurred in the dynamic creation process, usually because suitable parameters were not found.
DynamicsState(Vector, Vector) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
Constructs a new instance with the given position and momentum.

E

E() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets E, the residual matrix.
E() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets E, the residual matrix.
e_t(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets the residual of all subject at time t.
e_t(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets the residual of all subject at time t.
Edge<V> - Interface in dev.nm.graph
An edge connects a pair of vertices.
EdgeBetweeness<V> - Class in dev.nm.graph.community
The edge betweenness centrality is defined as the number of the shortest paths that go through an edge in a graph or network.
EdgeBetweeness(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Constructor for class dev.nm.graph.community.EdgeBetweeness
Computes the edge-betweeness-es of all edges in an undirected graph.
edges() - Method in class dev.nm.graph.community.EdgeBetweeness
Gets the set of all edges in the graph.
edges() - Method in interface dev.nm.graph.Graph
Gets the set of all edges in this graph.
edges() - Method in class dev.nm.graph.type.SparseGraph
 
edges() - Method in class dev.nm.graph.type.SparseTree
 
edges() - Method in class dev.nm.graph.type.VertexTree
 
edges(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
edges(V) - Method in interface dev.nm.graph.Graph
Gets the set of all edges associated with a vertex in this graph.
edges(V) - Method in class dev.nm.graph.type.SparseGraph
 
edges(V) - Method in class dev.nm.graph.type.SparseTree
 
eigen() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
Gets the eigenvalue decomposition of the correlation (or covariance) matrix.
Eigen - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
Given a square matrix A, an eigenvalue λ and its associated eigenvector v are defined by Av = λv.
Eigen(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Compute the eigenvalues and eigenvectors for a square matrix.
Eigen(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Use Eigen.Method.QR method by default.
Eigen(Matrix, Eigen.Method) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Compute the eigenvalues and eigenvectors for a square matrix.
Eigen(Matrix, Eigen.Method, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Compute the eigenvalues and eigenvectors for a square matrix.
EIGEN - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
the EIGEN test
Eigen.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
the available methods to compute eigenvalues and eigenvectors
eigenbasis() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get the eigenvectors.
EigenBoundUtils - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Utility methods for computing bounds of eigenvalues.
EigenCount - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Counts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than a given value x.
EigenCount(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount
Creates an instance for a symmetric tridiagonal matrix T.
EigenCountInRange - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Finds the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given interval.
EigenCountInRange(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange
Creates an instance for counting the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given interval.
EigenDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
Let A be a square, diagonalizable N × N matrix with N linearly independent eigenvectors.
EigenDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
Runs the eigen decomposition on a square matrix.
EigenDecomposition(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
Runs the eigen decomposition on a square matrix.
EigenProperty - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
EigenProperty is a read-only structure that contains the information about a particular eigenvalue, such as its multiplicity and eigenvectors.
eigenvalue() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get the eigenvalue.
EigenvalueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
Computes all the eigenvalues of a symmetric tridiagonal matrix.
EigenvalueByDQDS(TridiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS
Computes all the eigenvalues of a symmetric tridiagonal matrix.
eigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get an eigenvector.
ELECTRIC_EPSILON0 - Static variable in class dev.nm.misc.PhysicalConstants
The electric permittivity or electrical constant \(\epsilon_0\) in farads per meter (F m-1).
ELECTRON_MASS_ME - Static variable in class dev.nm.misc.PhysicalConstants
The electron rest mass \(m_e\) in kilograms (kg).
ELECTRON_VOLT_EV - Static variable in class dev.nm.misc.PhysicalConstants
The electron volt \(eV\) in joules (J).
ELEMENTARY_CHARGE_E - Static variable in class dev.nm.misc.PhysicalConstants
The elementary charge \(e\) in coulombs (C).
ElementaryFunction - Class in dev.nm.number.complex
This class contains some elementary functions for complex number, Complex.
ElementaryFunction() - Constructor for class dev.nm.number.complex.ElementaryFunction
 
ElementaryOperation - Class in dev.nm.algebra.linear.matrix.doubles.operation
There are three elementary row operations which are equivalent to left multiplying an elementary matrix.
ElementaryOperation(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Construct a transformation matrix of elementary operations.
ElementaryOperation(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Construct a transformation matrix of elementary operations.
ElementaryOperation(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Transform A by elementary operations.
elementDivide(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
 
elementMultiply(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
 
elementOperation(Matrix, Matrix, BivariateRealFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
 
eliminate(GLMProblem, Matrix) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination.Step
 
eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
 
eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue
 
EliminationByAIC - Class in dev.nm.stat.regression.linear.glm.modelselection
In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC.
EliminationByAIC() - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
 
EliminationByZValue - Class in dev.nm.stat.regression.linear.glm.modelselection
In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level).
EliminationByZValue(double) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue
Creates an instance with the given significance level [0, 1].
Elliott2005DLM - Class in tech.nmfin.meanreversion.elliott2005
This class implements the Kalman filter model as in Elliott's paper.
Elliott2005DLM(double, double, double, double, double) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Constructs an Elliott's Kalman filter model.
Elliott2005DLM(double, double, double, double, double, RandomStandardNormalGenerator) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Constructs an Elliott's Kalman filter model.
Elliott2005DLM(Elliott2005DLM) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Copy constructor.
ElliottOnlineFilter - Class in tech.nmfin.meanreversion.elliott2005
It is important to note that this algorithm does not guarantee that A > 0 0 < B < 1 Therefore, we need to check the outputs.
ElliottOnlineFilter(double[], double, double, double, double, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
 
ElliottOnlineFilter(double[], Elliott2005DLM, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
 
ElliottOnlineFilter.NoModelFitted - Exception in tech.nmfin.meanreversion.elliott2005
 
EmpiricalACER - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
This class contains empirical ACER \(\hat{\epsilon_k}(\eta_i)\) values and other related statistics estimated from observations.
EmpiricalACER(double[][], double[], double, double, double[][], double[], double[]) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
 
EmpiricalACEREstimation - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
This class estimates empirical ACER values from the given observations.
EmpiricalACEREstimation() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
Create an instance with default values.
EmpiricalACEREstimation(int, boolean, EpsilonStatisticsCalculator, int) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
Create an instance for counting empirical ACERs.
EmpiricalACERStatistics - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
This class contains the computed statistics of the estimated ACERs.
EmpiricalACERStatistics(double[], double[]) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
Create an instance for storing the computed statistics for estimated epsilon for various barrier levels.
EmpiricalDistribution - Class in dev.nm.stat.distribution.univariate
An empirical cumulative probability distribution function is a cumulative probability distribution function that assigns probability 1/n at each of the n numbers in a sample.
EmpiricalDistribution(double[]) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Construct an empirical distribution from a sample using the default quantile type Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED.
EmpiricalDistribution(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Construct an empirical distribution from a sample.
end() - Method in class dev.nm.interval.Interval
Get the end of this interval.
end() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
energy(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
 
entropy() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
entropy() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
Gets the entropy of this distribution.
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
entropy() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the entropy of this distribution.
entropy() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
entropy() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
entropy() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
entropy() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
entropy() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
entropy() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated.
entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated.
Entry(double, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.Realization.Entry
Construct an instance of TimeSeries.Entry.
Entry(double, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.MultivariateRealization.Entry
Construct an instance of TimeSeries.Entry.
Entry(int, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
 
Entry(int, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries.Entry
Construct an instance of TimeSeries.Entry.
Entry(int, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries.Entry
Construct an instance of Entry.
Entry(MatrixCoordinate, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
Construct a sparse entry in a sparse matrix.
Entry(T, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
Construct an instance of Entry.
Entry(T, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
Construct an instance of TimeSeries.Entry.
epsilon - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
the convergence tolerance
epsilon - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
epsilon - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
epsilon - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon() - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
Get the threshold to check whether a variable is an integer.
epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
EPSILON - Static variable in class dev.nm.misc.Constants
the default epsilon used in this library
epsilon1 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
 
epsilon2 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
 
EpsilonStatisticsCalculator - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical
Compute statistics: mean, confidence interval of estimated ACER values \(\epsilon_k(\eta_i)\).
EpsilonStatisticsCalculator(boolean, double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator
Create an instance with the weighting option and confidence interval.
equal(double[][], double[][], double) - Static method in class dev.nm.number.DoubleUtils
Check if two 2D arrays, double[][], are close enough, hence equal, entry-by-entry.
equal(double[], double[], double) - Static method in class dev.nm.number.DoubleUtils
Check if two double arrays are close enough, hence equal, entry-by-entry.
equal(double, double, double) - Static method in class dev.nm.number.DoubleUtils
Check if two doubles are close enough, hence equal.
equal(int[], int[]) - Static method in class dev.nm.number.DoubleUtils
Check if two int arrays, int[], are equal, entry-by-entry.
equal(Number, Number, double) - Static method in class dev.nm.number.NumberUtils
Check the equality of two Numbers, up to a precision.
EQUAL - dev.nm.interval.IntervalRelation
X is equal to Y.
EQUAL - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
two-sample; two-sided
EQUALITY - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the equality constraints
EqualityConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
The domain of an optimization problem may be restricted by equality constraints.
EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
Constructs the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ..., q\).
EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.EqualityConstraints
Constructs the equality constraints for a dual SOCP problem, \[ \max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{{A^q}_i'y + s_i = c^q_i}, s_i \in K_i, i = 1, 2, ..., q \\ \mathbf{{A^{\ell}}^T y + z^{\ell} = c^{\ell}} \\ \mathbf{{A^{u}}^T y = c^{u}} \\ \]
EqualityConstraints(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
Construct the equality constraints for a dual SDP problem, \(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).
EquallySpacedVariable(double, double) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid.EquallySpacedVariable
Create a new instance which specifies the position of the first element and the spacing along a dimension as the given values.
equals(SparseMatrix, SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Checks if two SparseMatrixs are equal.
equals(Graph<V, ?>, Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
Check if two graphs are equal in terms of node values and edges.
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
equals(Object) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
equals(Object) - Method in class dev.nm.analysis.function.tuple.Pair
 
equals(Object) - Method in class dev.nm.interval.Interval
 
equals(Object) - Method in class dev.nm.interval.Intervals
 
equals(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
 
equals(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
equals(Object) - Method in class dev.nm.misc.datastructure.SortableArray
 
equals(Object) - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
equals(Object) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
equals(Object) - Method in class dev.nm.number.complex.Complex
 
equals(Object) - Method in class dev.nm.number.Real
 
equals(Object) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
 
equals(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Check if two BigDecimals are equal up to a precision.
Erf - Class in dev.nm.analysis.function.special.gaussian
The Error function is defined as: \[ \operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt \]
Erf() - Constructor for class dev.nm.analysis.function.special.gaussian.Erf
 
Erfc - Class in dev.nm.analysis.function.special.gaussian
This complementary Error function is defined as: \[ \operatorname{erfc}(x) = 1-\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt \]
Erfc() - Constructor for class dev.nm.analysis.function.special.gaussian.Erfc
 
ErfInverse - Class in dev.nm.analysis.function.special.gaussian
The inverse of the Error function is defined as: \[ \operatorname{erf}^{-1}(x) \]
ErfInverse() - Constructor for class dev.nm.analysis.function.special.gaussian.ErfInverse
 
ErgodicHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
A simple decorator which will randomly vary dt between each sample.
ErgodicHybridMCMC(double, double, RandomLongGenerator, AbstractHybridMCMC) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
Constructs a new instance where dt is uniformly drawn from a given range.
ErgodicHybridMCMC(double, UnivariateRealFunction, AbstractHybridMCMC) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
Constructs a new instance where dt is given as a function.
error(T) - Method in interface dev.nm.solver.multivariate.minmax.MinMaxProblem
e(x, ω) is the error function, or the minmax objective, for a given ω.
estimate(double[][]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation
Estimate epsilon (or ACERs) from the given observations (each row is for one period).
estimate(int) - Method in interface dev.nm.stat.random.MeanEstimator
Gets an estimator.
estimate(int) - Method in class dev.nm.stat.random.variancereduction.AntitheticVariates
 
estimate(int) - Method in class dev.nm.stat.random.variancereduction.CommonRandomNumbers
 
estimate(int) - Method in class dev.nm.stat.random.variancereduction.ControlVariates
 
estimate(int) - Method in class dev.nm.stat.random.variancereduction.ImportanceSampling
 
estimate(Matrix) - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
 
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments
 
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
 
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
 
estimate(Matrix) - Method in class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
 
estimate(Matrix) - Method in interface tech.nmfin.returns.moments.ReturnsMoments.Estimator
Estimates the moments from a given returns matrix.
EstimateByLogLikelihood - Class in dev.nm.stat.evt.evd.univariate.fitting
Result from maximum likelihood fitting algorithm, which contains: the log-likelihood function, the fitted parameters for the target model, the variance-covariance matrix, the standard errors, the confidence intervals.
EstimateByLogLikelihood(Vector, RealScalarFunction) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
 
estimated_lambda - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
estimated lambda
estimatedPopulationEigenvalues(Vector) - Method in class dev.nm.stat.covariance.nlshrink.TauEstimator
Estimates population eigenvalues from given sample eigenvalues.
estimateForMultiPeriods(double[][]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
Estimate for multiple periods.
estimateForOnePeriod(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting
Estimate for a single period.
Estimator - Interface in dev.nm.stat.random
 
eta() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
 
EULER_MASCHERONI - Static variable in class dev.nm.misc.Constants
the Euler-Mascheroni constant
EulerMethod - Class in dev.nm.analysis.differentialequation.ode.ivp.solver
The Euler method is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.
EulerMethod(double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod
Constructs an Euler's method instance, with the given step size.
EulerMethod(int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod
Constructs an Euler's method instance, with the given number of steps.
EulerSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
The Euler scheme is the first order approximation of an SDE.
EulerSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
Discretize a continuous-time SDE using the Euler scheme.
evaluate() - Method in class dev.nm.stat.covariance.nlshrink.quest.QuEST
 
evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
 
evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
 
evaluate(double) - Method in class dev.nm.analysis.differentiation.Ridders
Evaluate f'(x), where f is a UnivariateRealFunction.
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DBetaRegularized
Evaluate \({\partial \over \partial x} \mathrm{B_x}(p, q)\).
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DErf
 
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.Dfdx
 
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGamma
 
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGaussian
 
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
 
evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toConstantMatrix
 
evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
Evaluate f(x) = A.
evaluate(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial
Evaluate this polynomial at x.
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
Evaluate the function f at x.
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
Evaluate y = f(x).
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
 
evaluate(double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.UnivariateRealFunction
Evaluate y = f(x).
evaluate(double) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
 
evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
Evaluate Ix(p,q).
evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularizedInverse
Evaluate \(I^{-1}_{(p,q)}(u)\).
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Digamma
 
evaluate(double) - Method in interface dev.nm.analysis.function.special.gamma.Gamma
Evaluate \(\Gamma(z) = \int_0^\infty e^{-t} t^{z-1} dt\).
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaGergoNemes
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczos
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.LogGamma
Evaluate the log of the Gamma function in the positive real domain.
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Trigamma
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalHastings
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalInverse
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalMarsaglia
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erf
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erfc
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.ErfInverse
 
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
 
evaluate(double) - Method in interface dev.nm.analysis.function.special.gaussian.StandardCumulativeNormal
Evaluate \(F(x;\,\mu,\sigma^2)\).
evaluate(double) - Method in interface dev.nm.analysis.sequence.Summation.Term
Evaluate the term for an index.
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction
Compute the epsilon value.
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERInverseFunction
 
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERLogFunction
Compute the log-scale epsilon value at the given barrier level.
evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnLevel
Compute the return level from the given return period.
evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnPeriod
 
evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
 
evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
Evaluate this function, f, based on only the current value of the stochastic process.
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
Get the i-th auto-correlation matrix.
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
Get the i-th auto-covariance matrix.
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
Get the i-th auto-correlation.
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
Gets the i-th auto-covariance.
evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta
 
evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
 
evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.DBeta
Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
evaluate(double, double) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
Evaluate f(x1, x2) = A.
evaluate(double, double) - Method in interface dev.nm.analysis.function.rn2r1.BivariateRealFunction
Evaluate y = f(x1,x2).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.Beta
Evaluate B(x,y).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.LogBeta
Compute log(B(x,y)).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaLowerIncomplete
Evaluate \(\gamma(s,x)\).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedP
Evaluate P(s,x).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedPInverse
Evaluate x = P-1(s,u).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedQ
Evaluate Q(s,x).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaUpperIncomplete
Compute Γ(s,x).
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
 
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
 
evaluate(double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMomentsEstimator
 
evaluate(double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
 
evaluate(double, double, double) - Method in interface dev.nm.analysis.function.rn2r1.TrivariateRealFunction
Evaluate y = f(x1,x2,x3).
evaluate(double, Vector) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction
Computes the derivative at the given point, x.
evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Bt
 
evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
Compute the function value at the i-th time point, \(f(\mathfrak{F_{t_i}})\).
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
Compute the auto-correlation for lag k.
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
Compute the auto-covariance for lag k.
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
Compute the partial auto-correlation for lag k.
evaluate(D) - Method in interface dev.nm.analysis.function.Function
Evaluate the function f at x, where x is from the domain.
evaluate(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
Evaluate numerically the partial derivative of f at point x.
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.Ridders
Evaluate the function f at x, where x is from the domain.
evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
 
evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.R1Projection
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.special.beta.MultinomialBetaFunction
 
evaluate(Vector) - Method in class dev.nm.analysis.function.special.Rastrigin
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Computes the final objective function value.
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
 
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
 
evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
 
evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
Note: x here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
 
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
Note: x here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
 
evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
Evaluate numerically the partial derivative of f at point x with step size h.
evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.Ridders
Evaluate numerically the derivative of f at point x, f'(x), with step size h.
evaluate(Vector, Vector) - Method in interface dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings.ProposalDensityFunction
Evaluates the density at the given points.
evaluate(Complex) - Method in class dev.nm.analysis.function.polynomial.Polynomial
Evaluate this polynomial at x.
evaluate(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
Evaluates the constraints.
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
 
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
 
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
 
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
 
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedRealFunction
Evaluate this function, f, at time t.
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction
Evaluate this function, f, at time t.
evaluate(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.FtAdaptedFunction
Evaluate this function, f, at time t.
evaluate(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
 
evaluate(Realization[]) - Method in interface dev.nm.stat.cointegration.JohansenAsymptoticDistribution.F
F(B).
evaluate(Number) - Method in class dev.nm.analysis.function.polynomial.Polynomial
Evaluate this polynomial at x.
evaluate(BigDecimal) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction
Evaluate z.
evaluate(X) - Method in interface dev.nm.stat.distribution.discrete.ProbabilityMassFunction
Compute the probability mass for a discrete realization x.
EvaluationException(String) - Constructor for exception dev.nm.analysis.function.Function.EvaluationException
Constructs an EvaluationException with the specified detail message.
EvenlySpacedGrid - Class in dev.nm.stat.stochasticprocess.timegrid
This is an evenly spaced time grid.
EvenlySpacedGrid(double, double, int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
Construct an evenly spaced time grid.
EXACT - dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
This is the exact distribution for Fisher's exact test.
ExceptionUtils - Class in dev.nm.misc
Exception-related utility functions.
execute(Runnable) - Method in class dev.nm.misc.parallel.Mutex
The runnable is executed under synchronization of this Mutex instance.
executeAll(Callable<T>...) - Method in class dev.nm.misc.parallel.ParallelExecutor
Executes an arbitrary number of Callable tasks, and returns a list of results in the same order.
executeAll(List<? extends Callable<T>>) - Method in class dev.nm.misc.parallel.ParallelExecutor
Executes a list of Callable tasks, and returns a list of results in the same sequential order as tasks.
executeAny(Callable<T>...) - Method in class dev.nm.misc.parallel.ParallelExecutor
Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
executeAny(List<? extends Callable<T>>) - Method in class dev.nm.misc.parallel.ParallelExecutor
Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
exp(double) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute ex.
exp(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the exponentials of values.
exp(double, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute ex.
exp(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the exponential of a vector, element-by-element.
exp(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Exponential of a complex number.
exp(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute ex.
exp(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute ex.
ExpectationAtEndTime - Class in dev.nm.stat.stochasticprocess.univariate.random
This class computes the expectation (mean) and the variance of a stochastic process, by Monte Carlo simulation, at the end of an interval: \(E(X_T)\).
ExpectationAtEndTime(RandomProcess, int, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
Compute the expectation of a random process at the end time.
ExpectationAtEndTime(RandomRealizationGenerator, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
Compute the expectation of a random process at the end time.
ExpectationAtEndTime(SDE, double, double, int, double, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
Compute the expectation of a stochastic SDE at the end time.
expectedPnL() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
Gets the expected PnL when playing the H strategy in mean-reverting model.
ExplicitCentralDifference1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
This explicit central difference method is a numerical technique for solving the one-dimensional wave equation by the following explicit three-point central difference equation.
ExplicitCentralDifference1D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
 
ExplicitCentralDifference2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
This explicit central difference method is a numerical technique for solving the two-dimensional wave equation by the following explicit three-point central difference equation.
ExplicitCentralDifference2D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
 
ExplicitImplicitModelPCA - Class in dev.nm.stat.factor.implicitmodelpca
Given a time series of vectored observations, we decompose them into a reduced dimension of linear sum of both explicit/specified and implicit factors.
ExplicitImplicitModelPCA(Matrix, Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
 
ExplicitImplicitModelPCA(Matrix, Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
 
ExplicitImplicitModelPCA.Result - Class in dev.nm.stat.factor.implicitmodelpca
 
expm1(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the exponential-minus-one (ex - 1) of values.
exponent() - Method in class dev.nm.number.ScientificNotation
Get the exponent.
Exponential - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially.
Exponential(double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
Construct an Exponential substitution rule.
ExponentialDistribution - Class in dev.nm.stat.distribution.univariate
The exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate.
ExponentialDistribution() - Constructor for class dev.nm.stat.distribution.univariate.ExponentialDistribution
Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
ExponentialDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.ExponentialDistribution
Construct an exponential distribution.
ExponentialFamily - Class in dev.nm.stat.distribution.univariate.exponentialfamily
The exponential family is an important class of probability distributions sharing this particular form.
ExponentialFamily(UnivariateRealFunction, RealVectorFunction, AbstractR1RnFunction, RealScalarFunction) - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.ExponentialFamily
Construct a factory to construct probability distribution in the exponential family of this form.
ExponentialMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Exponential distribution to model the observations.
ExponentialMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
Constructs an Exponential distribution for each state in the HMM model.
ExpTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
Logarithmic decay, where \(T_k = T_0 * 0.95^k\).
ExpTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
Constructs a new instance with an initial temperature.
exsec(double) - Static method in class dev.nm.geometry.TrigMath
Returns the exsecant of an angle.
extractPeaks(double[]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils
Extract peaks (values which are preceded and followed by values smaller than itself).
extrema() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
 
ExtremalGeneralizedEigenvalueByGreedySearch - Class in tech.nmfin.meanreversion.daspremont2008
Solves \[ \min_x \frac{x'Ax}{x'Bx} \\ \textup{s.t.,} \mathbf{Card}(x) \leqslant k, \left \| x \right \| = 1 \]
ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
Constructs the problem described in Section 3.2, d'Aspremont (2008), changed to a minimization problem.
ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP - Class in tech.nmfin.meanreversion.daspremont2008
Solves the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Constructs the problem described in Section 3.2, d'Aspremont (2008), changed to a minimization problem.
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, int, double, double, double, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueSolver - Interface in tech.nmfin.meanreversion.daspremont2008
 
ExtremalIndexByClusterSizeReciprocal - Class in dev.nm.stat.evt.exi
This class estimates the extremal index by the reciprocal of the average cluster size.
ExtremalIndexByClusterSizeReciprocal(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
 
ExtremalIndexByClusterSizeReciprocal(double, int) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
Create an instance with the given threshold and clustering interval length.
ExtremalIndexByFerroSeegers - Class in dev.nm.stat.evt.exi
This class estimates the extremal index from observations by the algorithm proposed by Ferro and Seegers.
ExtremalIndexByFerroSeegers(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers
Create an instance with the given threshold.
ExtremalIndexEstimation - Interface in dev.nm.stat.evt.exi
The extremal index \(\theta \in [0,1]\) characterizes the degree of local dependence in the extremes of a stationary time series.
ExtremeValueMC - Class in dev.nm.stat.evt.markovchain
Simulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions.
ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType) - Constructor for class dev.nm.stat.evt.markovchain.ExtremeValueMC
Create an instance with a given bivariate distribution that defines the dependence structure between two consecutive simulated values, and uses UniformRNG for random number generation.
ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType, RandomNumberGenerator) - Constructor for class dev.nm.stat.evt.markovchain.ExtremeValueMC
Create an instance with a given bivariate distribution that defines the dependence structure between two consecutive simulated values, and a uniform random number generator.
ExtremeValueMC.MarginalDistributionType - Enum in dev.nm.stat.evt.markovchain
Types of marginal distribution of each simulated value.
Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
 
Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
 
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
 
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
 
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
 
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
 
Ey(Vector) - Method in interface dev.nm.stat.regression.linear.LinearModel
Computes the expectation \(E(y(x))\) given an input.
Ey(Vector) - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
Calculates the probability of occurrence (y = 1).
Ey(Vector) - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
 
Ey(Vector, Vector, boolean) - Static method in class dev.nm.stat.regression.linear.ols.OLSRegression
 

F

f - Variable in class dev.nm.analysis.function.SubFunction
the original, unrestricted function
f - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
f - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
f - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
f - Variable in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
the integrand
f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
 
f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
f() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
 
f() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
f() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
f() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
f() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
 
f() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
 
f() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
 
f() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
Get the objective function.
f() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
 
f() - Method in interface dev.nm.solver.problem.OptimProblem
Get the objective function.
f() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation
Gets the transformation.
f(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the value of the initial condition of u at x.
f(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the initial condition of u at a given position x.
f(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the initial condition of u at the given position x.
f(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
The forcing term.
f(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the initial condition of u at the given point (x,y).
f(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the initial condition of u at the given point (x,y).
f(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets the implicit factor values at time t.
f(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets the factor values at time t.
F - Class in dev.nm.stat.test.variance
The F-test tests whether two normal populations have the same variance.
F - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
the scaling factor
F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
unique dis_G_list
F(double[], double[]) - Constructor for class dev.nm.stat.test.variance.F
Perform the F-test to test for equal variance of two normal populations.
F(double[], double[], double) - Constructor for class dev.nm.stat.test.variance.F
Perform the F-test to test for equal variance of two normal populations.
F() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
Get the differential, \(y^{(n)} = F\).
F() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
Gets the sample constant correlation matrix F.
F() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets F, the implicit factor value matrix.
F() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets F, the factor value matrix.
F(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Gets F(t), the coefficient matrix of xt.
F(int) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Get F(t), the coefficient of xt.
F_idx - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
indices for dis_G_list which is unique
F_Sum_BtDt - Class in dev.nm.stat.stochasticprocess.univariate.filtration
This represents a function of this integral \[ I = \int_{0}^{1} B(t)dt \]
F_Sum_BtDt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
 
F_Sum_tBtDt - Class in dev.nm.stat.stochasticprocess.univariate.filtration
This represents a function of this integral \[ \int_{0}^{1} (t - 0.5) * B(t) dt \]
F_Sum_tBtDt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
 
FactorAnalysis - Class in dev.nm.stat.factor.factoranalysis
Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors.
FactorAnalysis(Matrix, int) - Constructor for class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Performs factor analysis on the data set, using Bartlett's weighted least-squares scores, and sample correlation matrix.
FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule) - Constructor for class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Performs factor analysis on the data set with a user defined scoring rule.
FactorAnalysis(Matrix, int, FactorAnalysis.ScoringRule, Matrix) - Constructor for class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Performs factor analysis on the data set with a user defined scoring rule and a user defined covariance (or correlation) matrix.
FactorAnalysis.ScoringRule - Enum in dev.nm.stat.factor.factoranalysis
These are the different ways to compute the factor analysis scores.
factorial(int) - Static method in class dev.nm.analysis.function.FunctionOps
n!
factorial(int) - Static method in class dev.nm.number.big.BigIntegerUtils
Compute the n factorial.
factory - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
factoryCtor - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
FAEstimator - Class in dev.nm.stat.factor.factoranalysis
These are the estimators (estimated psi, loading matrix, scores, degrees of freedom, test statistics, p-value, etc.) from the factor analysis MLE optimization.
family() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
Gets the quasi-family specification.
FARADAY_F - Static variable in class dev.nm.misc.PhysicalConstants
The Faraday constant \(F\) in coulombs per mole (C mol-1).
FastAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
Matlab default: @annealingfast - The step has length temperature, with direction uniformly at random.
FastAnnealingFunction(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
Constructs a new instance where the RVG is created from a given RLG.
FastAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
Constructs a new instance that uses a given RVG.
FastKroneckerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
This is a fast and memory-saving implementation of computing the Kronecker product.
FastKroneckerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
Construct a Kronecker product for read-only.
FastTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
Linear decay, where \(T_k = T_0 / k\).
FastTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
Constructs a new instance with an initial temperature.
FDistribution - Class in dev.nm.stat.distribution.univariate
The F distribution is the distribution of the ratio of two independent chi-squared variates.
FDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.FDistribution
Construct an F distribution.
fdx(UnivariateRealFunction) - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
Get the integrand in the "transformed" integral, g(t) = f(x(t)) * x'(t).
FerrisMangasarianWrightPhase1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem.
FerrisMangasarianWrightPhase1(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
Construct the phase 1 algorithm for an infeasible table corresponding to a non-standard linear programming problem, e.g., b ≥ 0.
FerrisMangasarianWrightPhase2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table.
FerrisMangasarianWrightPhase2() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
FerrisMangasarianWrightPhase2(SimplexPivoting) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
Construct an LP solver to solve canonical LP problems using the Phase 2 algorithm in Ferris, Mangasarian & Wright.
FerrisMangasarianWrightScheme2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
The scheme 2 procedure removes equalities and free variables.
FerrisMangasarianWrightScheme2(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
Construct the scheme 2 algorithm for a table with equalities and free variables.
Fibonacci - Class in dev.nm.analysis.sequence
A Fibonacci sequence starts with 0 and 1 as the first two numbers.
Fibonacci(int) - Constructor for class dev.nm.analysis.sequence.Fibonacci
Construct a Fibonacci sequence.
FibonaccMinimizer - Class in dev.nm.root.univariate.bracketsearch
The Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio.
FibonaccMinimizer(double, int) - Constructor for class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer
Construct a univariate minimizer using the Fibonacci method.
FibonaccMinimizer.Solution - Class in dev.nm.root.univariate.bracketsearch
This is the solution to a Fibonacci's univariate optimization.
Field<F> - Interface in dev.nm.algebra.structure
As an algebraic structure, every field is a ring, but not every ring is a field.
Field.InverseNonExistent - Exception in dev.nm.algebra.structure
This is the exception thrown when the inverse of a field element does not exist.
fillInStackTrace() - Method in error dev.nm.misc.license.LicenseError
 
Filter - Interface in dev.nm.dsp.univariate.operation.system.doubles
A filter, for signal processing, takes (real) input signal and transforms it to (real) output signal.
filtering(double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Filter the observations without control variable.
filtering(double[], double[]) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Filter the observations.
filtering(MultivariateIntTimeTimeSeries) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Filter the observations without control variable.
filtering(MultivariateIntTimeTimeSeries, MultivariateIntTimeTimeSeries) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Filter the observations.
filterPrices(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
Filters out invalid prices.
filterUnchangedPrices(Matrix) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
Filters out prices that are unchanged between consecutive times.
Filtration - Class in dev.nm.stat.stochasticprocess.univariate.filtration
This class represents the filtration information known at the end of time.
Filtration(UnivariateTimeSeries<Double, ? extends UnivariateTimeSeries.Entry<Double>>) - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Construct a Filtration from a Brownian path.
FiltrationFunction - Class in dev.nm.stat.stochasticprocess.univariate.filtration
A filtration function, parameterized by a fixed filtration, is a function of time, \(f(\mathfrak{F_{t_i}})\).
FiltrationFunction() - Constructor for class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
 
findClusters(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
findFeasiblePoint(Matrix, Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
 
findPosition(VertexTree<T>) - Static method in class dev.nm.graph.type.VertexTree
Finds the position of a node from its tree root recursively.
findVertex(Matrix, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
 
FINE_STRUCTURE_ALPHA - Static variable in class dev.nm.misc.PhysicalConstants
The fine-structure constant \(\alpha\) (dimensionless).
FINISH - dev.nm.interval.IntervalRelation
X finishes Y
FINISH_INVERSE - dev.nm.interval.IntervalRelation
Y finishes X.
finishTime - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
 
finishTime() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
Gets the finish time, the time to finish visiting its sub-tree, of this node.
FINITE_DIFFERENCE - dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
The simplest choice is the three-point difference, not requiring constant interval lengths.
FINITE_DIFFERENCE - dev.nm.analysis.differentiation.univariate.Dfdx.Method
Finite difference: approximate a derivative using grid points.
FiniteDifference - Class in dev.nm.analysis.differentiation.univariate
A finite difference (divided by a small increment) is an approximation of the derivative of a function.
FiniteDifference(UnivariateRealFunction, int, FiniteDifference.Type) - Constructor for class dev.nm.analysis.differentiation.univariate.FiniteDifference
Construct an approximate derivative function for f using finite difference.
FiniteDifference.Type - Enum in dev.nm.analysis.differentiation.univariate
the available types of finite difference
first() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
FIRST - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
FirstGeneration - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
This interface allows customization of how the first pool of chromosomes is generated.
FirstOrderMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
This implements the steepest descent line search using the first order expansion of the Taylor's series.
FirstOrderMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
Construct a multivariate minimizer using the First-Order method.
FirstOrderMinimizer(FirstOrderMinimizer.Method, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
Construct a multivariate minimizer using the First-Order method.
FirstOrderMinimizer.Method - Enum in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
the available methods to do line search
FisherExactDistribution - Class in dev.nm.stat.test.distribution.pearson
Fisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics.
FisherExactDistribution(int[], int[], int) - Constructor for class dev.nm.stat.test.distribution.pearson.FisherExactDistribution
Construct the distribution for Fisher's exact test.
FisherExactDistribution(int[], int[], int, RandomLongGenerator) - Constructor for class dev.nm.stat.test.distribution.pearson.FisherExactDistribution
Construct the distribution for Fisher's exact test.
fit - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
fit(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.GEVFittingByMaximumLikelihood
Find the best-fit GEV parameters (location, scale, shape) by minimizing the negative log-likelihood.
fit(double[]) - Method in interface dev.nm.stat.evt.evd.univariate.fitting.MaximumLikelihoodFitting
Fit the model with the given observations.
fit(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThreshold
Fits the observations to a generalized Pareto distribution (GPD).
fit(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
 
fit(double[], double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
Fit the ACER function with OLS.
fit(double[], double[], double[], double, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
Fit the ACER function with the input values of barrier levels, epsilons, confidence intervals, and the mean of the peaks.
fit(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.CurveFitting
Fit a real valued function from a discrete set of data points.
fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite
 
fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
 
fit(OrderedPairs) - Method in interface dev.nm.analysis.curvefit.interpolation.univariate.Interpolation
Fit a real valued function from a discrete set of data points.
fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.LinearInterpolation
 
fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.NewtonPolynomial
 
fit(OrderedPairs) - Method in class dev.nm.analysis.curvefit.LeastSquares
 
fit(EmpiricalACER, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
Fit ACER function with empirical ACER estimates.
fit(GLMProblem, Vector) - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
Fits a Generalized Linear Model.
fit(GLMProblem, Vector) - Method in class dev.nm.stat.regression.linear.glm.IWLS
 
fit(GLMProblem, Vector) - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
fitModel(double[]) - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995MLE
Fits a KST model from returns.
fitness() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome
This is the fitness to determine how good this chromosome is.
fitness() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
 
fitted() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the fitted values, y^.
fittedValues() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
 
fittedValues() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
 
fittedValues() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
 
fittedValues(OLSResiduals[]) - Static method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
 
fitWithWeights(double[], double[], double[], double, double, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
Fit the ACER function with the input values of barrier levels, epsilons, confidence intervals, and the mean of the peaks.
fitWithWeightsAndInitial(double[], double[], double[], ACERFunction.ACERParameter, double, double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
 
FixedEffectsModel - Class in dev.nm.stat.regression.linear.panel
Fits the panel data to this linear model: \[ y_{it} = \alpha_{i}+X_{it}\mathbf{\beta}+u_{it} \] where \(y_{it}\) is the dependent variable observed for individual \(i\) at time \(t\), \(X_{it}\) is the time-variant \(1\times K\) regressor matrix, \(\alpha_{i}\) is the unobservable time-invariant individual effect and \(u_{it}\) is the error term.
FixedEffectsModel(PanelData, String, String[]) - Constructor for class dev.nm.stat.regression.linear.panel.FixedEffectsModel
Constructs a "within" fixed effects model from a panel of data.
FixedEffectsModel(PanelData, String, String[], PanelData.Transformation[]) - Constructor for class dev.nm.stat.regression.linear.panel.FixedEffectsModel
Constructs a "within" fixed effects model from a panel of data.
fixedSizeIntervals(LocalDateTime, LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Creates a list of intervals between start and end every so often.
fixing - Variable in class dev.nm.analysis.function.SubFunction
the restrictions or fixed values
fixing() - Method in interface dev.nm.solver.multivariate.constrained.ConstrainedOptimSubProblem
Gets the restrictions or fixed values;
flag_s() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
flag_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
FletcherLineSearch - Class in dev.nm.solver.multivariate.unconstrained.c2.linesearch
This is Fletcher's inexact line search method.
FletcherLineSearch() - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
Construct a line search minimizer using the Fletcher method with the default control parameters.
FletcherLineSearch(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
Construct a line search minimizer using the Fletcher method.
FletcherPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
This penalty function sums up the squared costs penalties.
FletcherPenalty(LessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
Construct a Fletcher penalty function from a collection of inequality constraints.
FletcherPenalty(LessThanConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
Construct a Fletcher penalty function from a collection of inequality constraints.
FletcherPenalty(LessThanConstraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
Construct a Fletcher penalty function from a collection of inequality constraints.
FletcherReevesMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
The Fletcher-Reeves method is a variant of the Conjugate-Gradient method.
FletcherReevesMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
Construct a multivariate minimizer using the Fletcher-Reeves method.
FlexibleTable - Class in dev.nm.misc.datastructure
This is a 2D table that can shrink or grow by row or by column.
FlexibleTable(int, int) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
Constructs a table using default labeling.
FlexibleTable(FlexibleTable) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
Copy constructor.
FlexibleTable(Object[], Object[]) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
Constructs a table by row and column labels, initializing the content to 0.
FlexibleTable(Object[], Object[], double[][]) - Constructor for class dev.nm.misc.datastructure.FlexibleTable
Constructs a flexible table that can shrink or grow.
FloatingLicenseServer - Class in dev.nm.misc.license
 
floatValue() - Method in class dev.nm.number.complex.Complex
 
floatValue() - Method in class dev.nm.number.Real
 
floatValue() - Method in class dev.nm.number.ScientificNotation
 
fmin - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the best minimum found so far
fmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
Fmin(double, int) - Static method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Compute the F critical value.
fminLast - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
fnext - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the next guess of the minimum
foreach(double[], UnivariateRealFunction) - Static method in class dev.nm.number.DoubleUtils
Apply a univariate function f to each element in an array.
foreach(Matrix, UnivariateRealFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a new matrix in which each entry is the result of applying a function to the corresponding entry of a matrix.
foreach(SparseVector, UnivariateRealFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a sparse vector.
foreach(Vector, UnivariateRealFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.
foreach(Vector, DoubleUnaryOperator) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.
forEach(Iterable<T>, IterationBody<T>) - Method in class dev.nm.misc.parallel.ParallelExecutor
Runs a "foreach" loop in parallel.
foreachColumn(Matrix, RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Constructs a vector in which each entry is the result of applying a RealScalarFunction to each column of an input matrix.
foreachColumn(Matrix, RealVectorFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a new matrix in which each column is the result of applying a real vector function on each column vector of an input matrix.
foreachRow(Matrix, RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Constructs a vector in which each entry is the result of applying a RealScalarFunction to each row of an input matrix.
foreachRow(Matrix, RealVectorFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a new matrix in which each row is the result of applying a real vector function on each row vector of an input matrix.
foreachVector(Vector[], RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Applies a RealScalarFunction on each input vector.
foreachVector(Vector[], RealVectorFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Applies a real vector function on each input vector.
foreachVector(Collection<Vector>, RealScalarFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Applies a RealScalarFunction on each input vector.
foreachVector(Collection<Vector>, RealVectorFunction) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Applies a real vector function on each input vector.
Forecast(int, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
 
Forest<V,​E extends HyperEdge<V>> - Interface in dev.nm.graph
A forest is a disjoint union of trees.
forLagOrder(int) - Method in interface dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCorrelationForObject
 
forLagOrder(int) - Method in interface dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.AutoCovarianceForObject
 
forLoop(int, int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
Runs a for-loop in parallel.
forLoop(int, int, LoopBody) - Method in class dev.nm.misc.parallel.ParallelExecutor
Calls forLoop with increment of 1.
forward(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
Perform a forward sweep.
FORWARD - dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
forward difference
ForwardBackwardProcedure - Class in dev.nm.stat.hmm
The forward-backward procedure is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables given a sequence of observations.
ForwardBackwardProcedure(HiddenMarkovModel, double[]) - Constructor for class dev.nm.stat.hmm.ForwardBackwardProcedure
Constructs the forward and backward probability matrix calculator for an HMM model.
ForwardBackwardProcedure(HiddenMarkovModel, int[]) - Constructor for class dev.nm.stat.hmm.ForwardBackwardProcedure
Constructs the forward and backward probability matrix calculator for an HMM model.
ForwardSelection - Class in dev.nm.stat.regression.linear.glm.modelselection
Constructs a GLM model for a set of observations using the forward selection method.
ForwardSelection(GLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection
Constructs a GLM model using the forward selection method, with SelectionByAIC as the default algorithm.
ForwardSelection(GLMProblem, ForwardSelection.Step) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection
Constructs a GLM model using the forward selection method.
ForwardSelection.Step - Interface in dev.nm.stat.regression.linear.glm.modelselection
 
ForwardSubstitution - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
Forward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L.
ForwardSubstitution() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
 
foundPositiveDefiniteHessian - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
FRECHET - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Frechet distribution.
FrechetDistribution - Class in dev.nm.stat.evt.evd.univariate
The Fréchet distribution is a special case (Type II) of the generalized extreme value distribution, with \(\xi>0\).
FrechetDistribution() - Constructor for class dev.nm.stat.evt.evd.univariate.FrechetDistribution
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\), shape \(\alpha=1\).
FrechetDistribution(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.FrechetDistribution
Create an instance with the given parameter values.
FREE - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the free variables
Frobenius(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the Frobenius norm, i.e., the sqrt of the sum of squares of all elements of a matrix.
fromContext(HouseholderContext, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
 
fromPolar(double, double) - Static method in class dev.nm.number.complex.Complex
Factory method to construct a complex number from the polar form: (r, θ).
Fstat() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: F statistics
ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
Get an array of the function values.
ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
 
ft() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
 
Ft - Class in dev.nm.stat.stochasticprocess.univariate.sde
This represents the concept 'Filtration', the information available at time t.
Ft() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Construct an empty filtration (no information).
Ft(Ft) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Copy constructor.
Ft() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
Compute all function values at all time points.
FtAdaptedFunction - Interface in dev.nm.stat.stochasticprocess.univariate.sde
This represents an Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
FtAdaptedRealFunction - Interface in dev.nm.stat.stochasticprocess.multivariate.sde
This represents an Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
FtAdaptedVectorFunction - Interface in dev.nm.stat.stochasticprocess.multivariate.sde
This represents a vector-valued Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.
FtWt - Class in dev.nm.stat.stochasticprocess.univariate.sde
This is a filtration implementation that includes the path-dependent information, Wt.
FtWt() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
Construct an empty filtration (no information).
FtWt(FtWt) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
Copy constructor.
fullMinimizer() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.IterativeSolution
Gets the minimizer to the original problem.
Function<D,​R> - Interface in dev.nm.analysis.function
The mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output).
Function.EvaluationException - Exception in dev.nm.analysis.function
This is the RuntimeException thrown when it fails to evaluate an expression.
FunctionOps - Class in dev.nm.analysis.function
These are some commonly used mathematical functions.
fw() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Evaluates \(E(w'r) - q * Var(w'r)\) at w_eff.
fx() - Method in exception dev.nm.analysis.root.univariate.NoRootFoundException
Get f(x).

G

g - Variable in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
 
g() - Method in interface dev.nm.analysis.differentiation.differentiability.C1
Get the gradient function, g, of a real valued function f.
g() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
 
g(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the value of the initial condition of the time derivative of u at x.
g(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
The boundary value function.
g(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the initial condition of the time derivative of u at the given point (x,y).
g(double, double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the boundary condition at the given boundary point (x,y) at the given time point t.
G(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Gets G(t), the coefficient matrix of xt - 1.
G(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Get G(t), the coefficient of xt - 1.
g1(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
The value of the linear combination of \(u\) and \(\frac{\partial u}{\partial x}\) at the boundary \(x = 0\) at a given time \(t\).
g2(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
The value of the linear combination of \(u\) and \(\frac{\partial u}{\partial x}\) at the boundary \(x = a\) at the given time \(t\).
g2(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the value of the linear combination of \(u\) and \(\frac{\partial u}{\partial x}\) at the boundary \(x = a\) at the given time \(t\).
gamma - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
gamma() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the estimated sequence of gamma (arc length).
gamma() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the AR coefficients of the lagged differences; null if p = 1
gamma(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the AR coefficient of the i-th lagged differences.
gamma(HiddenMarkovModel, int[], Matrix[]) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
Gets the (T-1 * N) γ matrix, where the (t, i)-th entry is γt(i).
Gamma - Interface in dev.nm.analysis.function.special.gamma
The Gamma function is an extension of the factorial function to real and complex numbers, with its argument shifted down by 1.
Gamma() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets Γ, the explicit factor loading matrix.
GammaDistribution - Class in dev.nm.stat.distribution.univariate
This gamma distribution, when k is an integer, is the distribution of the sum of k independent exponentially distributed random variables, each of which has a mean of θ (which is equivalent to a rate parameter of θ-1).
GammaDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.GammaDistribution
Construct a Gamma distribution.
GammaGergoNemes - Class in dev.nm.analysis.function.special.gamma
The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical.
GammaGergoNemes() - Constructor for class dev.nm.analysis.function.special.gamma.GammaGergoNemes
 
GammaLanczos - Class in dev.nm.analysis.function.special.gamma
Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise.
GammaLanczos() - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczos
Construct an instance of a Gamma function, computed using the Lanczos approximation.
GammaLanczos(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczos
Construct an instance of a Gamma function, computed using the Lanczos approximation.
GammaLanczosQuick - Class in dev.nm.analysis.function.special.gamma
Lanczos approximation, computations are done in double.
GammaLanczosQuick() - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
Construct an instance of a Gamma function, computed using the Lanczos approximation.
GammaLanczosQuick(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
Construct an instance of a Gamma function, computed using the Lanczos approximation.
GammaLowerIncomplete - Class in dev.nm.analysis.function.special.gamma
The Lower Incomplete Gamma function is defined as: \[ \gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s) \] P(s,x) is the Regularized Incomplete Gamma P function.
GammaLowerIncomplete() - Constructor for class dev.nm.analysis.function.special.gamma.GammaLowerIncomplete
 
GammaMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Gamma distribution to model the observations.
GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], boolean, boolean, double, int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
Constructs a Gamma distribution for each state in the HMM model.
GammaMixtureDistribution(GammaMixtureDistribution.Lambda[], int) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
Constructs a Gamma distribution for each state in the HMM model.
GammaMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
the Gamma distribution parameters
GammaRegularizedP - Class in dev.nm.analysis.function.special.gamma
The Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]
GammaRegularizedP() - Constructor for class dev.nm.analysis.function.special.gamma.GammaRegularizedP
 
GammaRegularizedPInverse - Class in dev.nm.analysis.function.special.gamma
The inverse of the Regularized Incomplete Gamma P function is defined as: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] When s > 1, we use the asymptotic inversion method. When s <= 1, we use an approximation of P(s,x) together with a higher-order Newton like method. In both cases, the estimated value is then improved using Halley's method, c.f., HalleyRoot.
GammaRegularizedPInverse() - Constructor for class dev.nm.analysis.function.special.gamma.GammaRegularizedPInverse
 
GammaRegularizedQ - Class in dev.nm.analysis.function.special.gamma
The Regularized Incomplete Gamma Q function is defined as: \[ Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0 \] The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.
GammaRegularizedQ() - Constructor for class dev.nm.analysis.function.special.gamma.GammaRegularizedQ
 
GammaUpperIncomplete - Class in dev.nm.analysis.function.special.gamma
The Upper Incomplete Gamma function is defined as: \[ \Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s) \] The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable.
GammaUpperIncomplete() - Constructor for class dev.nm.analysis.function.special.gamma.GammaUpperIncomplete
 
GARCH11Model - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
An GARCH11 model takes this form.
GARCH11Model(double, double, double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
Construct a GARCH(1,1) model.
GARCHFit - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
This implementation fits, for a data set, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model by maximizing the likelihood function using the gradient information.
GARCHFit(double[], int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
Fit a GARCH(p, q) model to a time series.
GARCHFit(double[], int, int, double, int, GARCHFit.GRADIENT) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
Fit a GARCH(p, q) model to a time series.
GARCHFit.GRADIENT - Enum in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
the available methods to compute the gradient to guild the optimization search
GARCHModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
The GARCH(p, q) model takes this form.
GARCHModel(double, double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Construct a GARCH model.
GARCHModel(GARCHModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Copy constructor.
GARCHResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
 
GARCHResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
 
GARCHResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
 
GARCHResamplerFactory2 - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
 
GARCHResamplerFactory2() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
GARCHResamplerFactory2(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
GARCHSim - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch
This class simulates the GARCH models of this form.
GARCHSim(GARCHModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
Simulate an GARCH model.
GARCHSim(GARCHModel, double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
Simulate an GARCH model.
GARCHSim(GARCHModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
Simulate an GARCH model.
GaussChebyshevQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
Gauss-Chebyshev Quadrature uses the following weighting function: \[ w(x) = \frac{1}{\sqrt{1 - x^2}} \] to evaluate integrals in the interval (-1, 1).
GaussChebyshevQuadrature(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussChebyshevQuadrature
Create an integrator of order n.
GaussHermiteQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
Gauss-Hermite quadrature exploits the fact that quadrature approximations are open integration formulas (that is, the values of the endpoints are not required) to evaluate of integrals in the range \((-\infty, \infty )\).
GaussHermiteQuadrature(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussHermiteQuadrature
Create an integrator of order n.
Gaussian - Class in dev.nm.analysis.function.special.gaussian
The Gaussian function is defined as: \[ f(x) = a e^{- { \frac{(x-b)^2 }{ 2 c^2} } } \]
Gaussian() - Constructor for class dev.nm.analysis.function.special.gaussian.Gaussian
Construct an instance of the standard Gaussian function: \(f(x) = e^{-{\frac{(x)^2}{2}}}\)
Gaussian(double, double, double) - Constructor for class dev.nm.analysis.function.special.gaussian.Gaussian
Construct an instance of the Gaussian function.
GAUSSIAN_JORDAN_ELIMINATION - dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
use Gauss-Jordan elimination; cheap but subject to numerical stability (rounding errors)
GaussianElimination - Class in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
The Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form.
GaussianElimination(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Run the Gaussian elimination algorithm with partial pivoting.
GaussianElimination(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Run the Gaussian elimination algorithm.
GaussianElimination4SquareMatrix - Class in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
This is a wrapper for GaussianElimination but applies only to square matrices.
GaussianElimination4SquareMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
Run the Gaussian elimination algorithm on a square matrix.
GaussianElimination4SquareMatrix(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
Run the Gaussian elimination algorithm on a square matrix.
GaussianProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
A proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix.
GaussianProposalFunction(double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
Constructs a Gaussian proposal function.
GaussianProposalFunction(double, int, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
Constructs a Gaussian proposal function.
GaussianProposalFunction(Matrix, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
Constructs a Gaussian proposal function.
GaussianQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
A quadrature rule is a method of numerical integration in which we approximate the integral of a function by a weighted sum of sample points.
GaussianQuadrature(GaussianQuadratureRule) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
Create a Gaussian quadrature integrator with the given quadrature rule.
GaussianQuadratureRule - Interface in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
This interface defines a Gaussian quadrature rule used in Gaussian quadrature.
GaussJordanElimination - Class in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
Gauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form.
GaussJordanElimination(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
Run the Gauss-Jordan elimination algorithm.
GaussJordanElimination(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
Run the Gauss-Jordan elimination algorithm.
GaussLaguerreQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
Gauss-Laguerre quadrature exploits the fact that quadrature approximations are open integration formulas (i.e.
GaussLaguerreQuadrature(int, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussLaguerreQuadrature
Create an integrator of order n.
GaussLegendreQuadrature - Class in dev.nm.analysis.integration.univariate.riemann.gaussian
Gauss-Legendre quadrature considers the simplest case of uniform weighting: \(w(x) = 1\).
GaussLegendreQuadrature(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussLegendreQuadrature
Create an integrator of order n.
GaussNewtonImpl(C2OptimProblem, RntoMatrix) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
 
GaussNewtonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
The Gauss-Newton method is a steepest descent method to minimize a real vector function in the form: /[ f(x) = [f_1(x), f_2(x), ..., f_m(x)]' /] The objective function is /[ F(x) = f' %*% f ]/
GaussNewtonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
Construct a multivariate minimizer using the Gauss-Newton method.
GaussNewtonMinimizer.MySteepestDescent - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
 
GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
an implementation of the Gauss-Newton algorithm.
GaussSeidelSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
Similar to the Jacobi method, the Gauss-Seidel method (GS) solves each equation in sequential order.
GaussSeidelSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
Construct a Gauss-Seidel (GS) solver.
GBMProcess - Class in dev.nm.stat.stochasticprocess.univariate.sde.process
A Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.
GBMProcess(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.GBMProcess
Construct a Geometric Brownian motion.
gcd(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
Calculates the greatest common divisor of integer a and integer b.
generalConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
GeneralConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
The real-valued constraints define the domain (feasible regions) for a real-valued objective function in a constrained optimization problem.
GeneralConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
Construct an instance of constraints from an array of real-valued functions.
GeneralConstraints(Collection<RealScalarFunction>) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
Construct an instance of constraints from a collection of real-valued functions.
GeneralEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
This is the collection of equality constraints for an optimization problem.
GeneralEqualityConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralEqualityConstraints
Constructs an instance of equality constraints from an array of real-valued functions.
GeneralEqualityConstraints(Collection<RealScalarFunction>) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralEqualityConstraints
Constructs an instance of equality constraints from a collection of real-valued functions.
GeneralGreaterThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
This is the collection of greater-than-or-equal-to constraints for an optimization problem.
GeneralGreaterThanConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
Construct an instance of greater-than-or-equal-to inequality constraints from an array of real-valued functions.
GeneralGreaterThanConstraints(Collection<RealScalarFunction>) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
Construct an instance of greater-than-or-equal-to inequality constraints from a collection of real-valued functions.
GeneralizedConjugateResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Generalized Conjugate Residual method (GCR) is useful for solving a non-symmetric n-by-n linear system.
GeneralizedConjugateResidualSolver(int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
Construct a GCR solver with restarts.
GeneralizedConjugateResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
Construct a full GCR solver.
GeneralizedConjugateResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
Construct a GCR solver with restarts.
GeneralizedEVD - Class in dev.nm.stat.evt.evd.univariate
Generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions.
GeneralizedEVD() - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Create an instance of generalized extreme value distribution with the default parameter values: location \(\mu=0\), scale \(\sigma=1\), shape \(\xi=0\).
GeneralizedEVD(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Create an instance of generalized extreme value distribution with the given parameters.
GeneralizedLinearModel - Class in dev.nm.stat.regression.linear.glm
The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares regression.
GeneralizedLinearModel(GLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Solves a generalized linear problem using the Iterative Re-weighted Least Squares algorithm.
GeneralizedLinearModel(GLMProblem, GLMFitting) - Constructor for class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Constructs a GeneralizedLinearModel instance.
GeneralizedLinearModelQuasiFamily - Class in dev.nm.stat.regression.linear.glm.quasi
GLM for the quasi-families.
GeneralizedLinearModelQuasiFamily(QuasiGLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
Constructs a GeneralizedLinearModelQuasiFamily instance.
GeneralizedMinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n linear system.
GeneralizedMinimalResidualSolver(int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
Construct a GMRES solver with restarts.
GeneralizedMinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
Construct a full GMRES solver.
GeneralizedMinimalResidualSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
Construct a GMRES solver with restarts.
GeneralizedParetoDistribution - Class in dev.nm.stat.evt.evd.univariate
Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold.
GeneralizedParetoDistribution() - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\), shape \(\xi=0\).
GeneralizedParetoDistribution(double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
Create an instance with zero location, and the given scale and shape parameters.
GeneralizedParetoDistribution(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
Create an instance with the given parameter values.
GeneralizedSimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.unconstrained.annealing
Tsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA).
GeneralizedSimulatedAnnealingMinimizer(int, double, double, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the Generalized Simulated Annealing minimizer.
GeneralizedSimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the Generalized Simulated Annealing minimizer with the recommended visiting and acceptance parameter.
GeneralizedSimulatedAnnealingMinimizer(int, StopCondition) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.GeneralizedSimulatedAnnealingMinimizer
Constructs a new instance of the Generalized Simulated Annealing minimizer.
GeneralLessThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.general
This is the collection of less-than or equal-to constraints for an optimization problem.
GeneralLessThanConstraints(RealScalarFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralLessThanConstraints
Construct an instance of less-than or equal-to inequality constraints from an array of real-valued functions.
GeneralLessThanConstraints(Collection<RealScalarFunction>) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.general.GeneralLessThanConstraints
Construct an instance of less-than or equal-to inequality constraints from a collection of real-valued functions.
generateAndReflectColumns(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Reflects a range of sub-columns with a Householder generated by the first column in the range.
generateAndReflectRows(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Reflects a range of sub-rows with a Householder generated by the first row in the range.
generateLeftHouseholder(int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Generates a left Householder from a sub-column of the underlying matrix, in order to zero out entries (except the first entry) of the sub-column.
generateRightHouseholder(int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Generates a right Householder from a sub-row of the underlying matrix, in order to zero out entries (except the first entry) of the sub-row.
generator - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
The vector which is used to generate the Householder vector.
GenericFieldMatrix<F extends Field<F>> - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
This is a generic matrix over a Field.
GenericFieldMatrix(int, int, F) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
Construct a matrix over a field.
GenericFieldMatrix(F[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
Construct a matrix over a field.
GenericMatrix<T extends GenericMatrix<T,​F>,​F extends Field<F>> - Interface in dev.nm.algebra.linear.matrix.generic
This class defines a matrix over a field.
GenericMatrixAccess<F extends Field<F>> - Interface in dev.nm.algebra.linear.matrix.generic
This interface defines the methods for accessing entries in a matrix over a field.
GenericTimeTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.univariate
This is a univariate time series indexed by some notion of time.
GenericTimeTimeSeries(T[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Construct a univariate time series from timestamps and values.
GeneticAlgorithm - Class in dev.nm.solver.multivariate.geneticalgorithm
A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
GeneticAlgorithm(RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Construct an instance of this implementation of genetic algorithm.
geometricMultiplicity() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty
Get the dimension of the vector space spanned by the eigenvectors.
get() - Method in class dev.nm.misc.parallel.Reference
 
get(double, int) - Method in class dev.nm.misc.datastructure.MathTable
Gets a particular table entry at [i,j].
get(double, String) - Method in class dev.nm.misc.datastructure.MathTable
Gets a particular table entry at [i, "header"].
get(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
get(int) - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
 
get(int) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
get(int) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
get(int) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
get(int) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Get the value at position i.
get(int) - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
Get the ordered pair at index i.
get(int) - Method in class dev.nm.analysis.sequence.Fibonacci
 
get(int) - Method in interface dev.nm.analysis.sequence.Sequence
Get the i-th entry in the sequence, counting from 1.
get(int) - Method in class dev.nm.interval.Intervals
Get the i-th interval.
get(int) - Method in class dev.nm.misc.datastructure.MathTable.Row
Gets the value in the row by column index.
get(int) - Method in class dev.nm.misc.datastructure.SortableArray
 
get(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
 
get(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
 
get(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Get the i-th value.
get(int) - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries
Get the value at time t (random access).
get(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Get the i-th value.
get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
 
get(int) - Method in interface dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries
Get the value at time t.
get(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
get(int...) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
 
get(int...) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
Returns the element at the specified position in this collection.
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
get(int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixAccess
Get the matrix entry at [i,j].
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets the value of an entry at (i,j) in the transformed matrix.
get(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
get(int, int) - Method in interface dev.nm.algebra.linear.matrix.generic.GenericMatrixAccess
Get the matrix entry at [i,j].
get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
get(int, int) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
get(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
Get the value of a table entry.
get(int, int) - Method in class dev.nm.misc.datastructure.FlexibleTable
 
get(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
Get the auto-correlation of Xi and Xj.
get(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
Get the auto-covariance matrix for Xi and Xj.
get(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
Get the auto-correlation of xi and xj.
get(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.AutoCovarianceFunction
Get the auto-covariance of xi and xj.
get(String) - Method in class dev.nm.misc.datastructure.MathTable.Row
Gets the value in the row by column name.
get(String) - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
 
get(T) - Method in class dev.nm.combinatorics.Ties
Get the number of occurrences of an object.
get(T) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Get the value at time t.
get0s(int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets n 0 vectors.
getAaa(int) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getACERs() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the estimated epsilon values for different barrier levels per period.
getActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Get the i-th active index.
getActiveConstraints(Vector, double) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Get the active constraint.
getActiveIndices() - Method in class dev.nm.misc.algorithm.ActiveSet
Get all active indices.
getActiveRows(Vector, double) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Get the active constraint indices.
getAll() - Method in class dev.nm.analysis.sequence.Fibonacci
 
getAll() - Method in interface dev.nm.analysis.sequence.Sequence
Get a copy of the whole (finite) sequence in double[].
getAllParts(Vector, Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction
Combines the variable and fixed values to form an input to the original function.
getAllSubjects() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the name of all subjects.
getAlternativeHypothesis() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.AndersonDarling
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.HypothesisTest
Get the description of the alternative hypothesis.
getAlternativeHypothesis() - Method in class dev.nm.stat.test.mean.OneWayANOVA
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.mean.T
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.KruskalWallis
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.SiegelTukey
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.VanDerWaerden
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
Deprecated.
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.Bartlett
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.BrownForsythe
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.F
 
getAlternativeHypothesis() - Method in class dev.nm.stat.test.variance.Levene
 
getAplus() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
 
getARMA() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
getARMAForecastOneStep() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets the auxiliary ARMA one-step ahead forecaster.
getARMAGARCHModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
 
getARMAModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the fitted ARMA model.
getARMAModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
Get the ARMA part of this model.
getARMAX() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
Get the auxiliary regression.
getAuxiliaryOLSRegression(Vector, LMResiduals) - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
 
getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
 
getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Glejser
 
getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.HarveyGodfrey
 
getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
Define the transformation of residuals.
getAuxiliaryRegression() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
 
getAverageClusterSize() - Method in class dev.nm.stat.evt.cluster.Clusters
Get the average cluster size.
getBarrierLevels() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the barrier levels used for estimation.
getBase() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best1Bin
 
getBase() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Pick a base chromosome from the population.
getBasis() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Get the orthogonal basis.
getBasis(int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.Basis
Get the full set of the standard basis vectors.
getBasis(int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.Basis
Get a subset of the standard basis vectors.
getBCol(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Get the table entry at [i, B].
getBeginIndex() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
Get the index of the first element of this cluster.
getBest(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Get the i-th best chromosome.
getBinKeyValues(RealScalarFunction) - Method in class dev.nm.misc.algorithm.Bins
Applies a function to the key of each bin.
getBinObjectValues(Function<List<T>, Double>) - Method in class dev.nm.misc.algorithm.Bins
Applies a function to the items of each bin.
getBins() - Method in class dev.nm.misc.algorithm.Bins
Divides the items into n bins.
getBuyThreshold() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Boundaries
Gets the buy threshold.
getCharacteristicPolynomial() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
Get the characteristic polynomial.
getChild(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Produce a child chromosome.
getChild(int) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
 
getChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
Get the set of vertices that have an incoming arc coming from a vertex.
getClusterCount() - Method in class dev.nm.stat.evt.cluster.Clusters
Get the number of clusters.
getClusterMaxima() - Method in class dev.nm.stat.evt.cluster.Clusters
Get an array of cluster maxima.
getClusters() - Method in class dev.nm.stat.evt.cluster.Clusters
Get the list of clusters.
getClusters(double[]) - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
Count clusters from the given observations.
getClusters(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
getCoefficient(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial
Get an-i, the coefficient of xn-i.
getCoefficients() - Method in class dev.nm.analysis.function.polynomial.Polynomial
Get a copy of the polynomial coefficients.
getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
 
getCoefficients() - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
Get the coefficients \(c_i\) associated with each evaluation point \(x_i\).
getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
 
getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
 
getCoefficients() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
 
getColLabel(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Gets the label for column i.
getColLabel(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
getColumn(int) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
Get the specified column in the matrix as a vector.
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Get a column.
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
getColumn(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
getColumn(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
 
getColumn(int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Gets a sub-column of the j-th column, from beginRow row to endRow row, inclusively.
getComplement() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Get the basis of the orthogonal complement.
getComplexRoots(List<? extends Number>) - Static method in class dev.nm.analysis.function.polynomial.root.PolyRoot
Get a copy of only the Complex but not real roots of a polynomial.
getComponent(List<double[]>, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
Gets the i-th component from a list of double[].
getConcurrency() - Method in class dev.nm.misc.parallel.ParallelExecutor
Returns the number of threads used for parallel execution.
getConfidenceInterval() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
Get the estimated confidence intervals of the fitted ACER function.
getConfidenceInterval() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
Get the width of half of the confidence interval, that is, the interval is mean +/- width.
getConfidenceLevel() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
Get the confidence level.
getConfidenceWidths() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the confidence interval half-widths for the estimates of the barrier levels.
getConstrainedOptimSubProblem(ConstrainedOptimProblem, Map<Integer, Double>) - Static method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
Gets the ConstrainedOptimSubProblem representation of the sub-problem.
getConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
Get the list of constraint functions.
getConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
Get the constraints.
getConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
 
getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
 
getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
 
getConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.EqualityConstraints
 
getContext(Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
Generates the context information from a generating vector x.
getCoordinate(Vector[], int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the vector entries from a particular coordinate.
getCoordinate(Collection<Vector>, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the vector entries from a particular coordinate.
getCoordinates() - Method in class dev.nm.geometry.Point
Get the coordinates of the point.
getCostRow(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Get the table entry at [COST, j].
getCount() - Method in class dev.nm.misc.algorithm.iterative.monitor.CountMonitor
Get the number of iterations.
getCovarianceMatrix() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
Gets the "shrunk" covariance matrix.
getCovariances(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.CovarianceEstimator
 
getCovariances(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
 
getCutter(ILPProblem) - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer.CutterFactory
Construct a new Cutter for a MILP problem.
getDemeanedModel() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Get the demeaned version of the time series model.
getDemeanedModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Get the demeaned version of the time series model.
getDifference(int) - Method in class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
Get the divided difference of the given order.
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
 
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
 
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
 
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl
 
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
 
getDirection(Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
Get the next search direction.
getDisjointGraphs(UnDiGraph<V, E>) - Static method in class dev.nm.graph.GraphUtils
 
getDisjointGraphs(UnDiGraph<V, E>, GraphUtils.GraphFactory<G>) - Static method in class dev.nm.graph.GraphUtils
Separate an undirected graph into disjointed connected graphs.
getDistribution() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
Get the univariate extreme value distribution for generating innovations.
getDistribution() - Method in class dev.nm.stat.hmm.mixture.MixtureHMM
Gets the distribution in the hidden Markov model.
getDistribution(int) - Method in enum dev.nm.stat.test.timeseries.adf.TrendType
Get an ADF distribution per sample size.
getDistribution(Vector) - Method in class dev.nm.stat.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
Construct a probability distribution in the exponential family.
getDistribution(Vector) - Method in class dev.nm.stat.distribution.univariate.exponentialfamily.ExponentialFamily
Construct a probability distribution in the exponential family.
getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
Finds the optimal weights for a diversified portfolio.
getDiversifiedWeights(Corvalan2005.WeightsConstraint, Vector, Matrix, Vector, EqualityConstraints, LessThanConstraints) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.Corvalan2005
Finds the optimal weights for a diversified portfolio.
getEdge(V, V, X) - Method in interface dev.nm.graph.GraphUtils.EdgeFactory
Creates an edge between two nodes.
getEdgeBetweeness(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Method in interface dev.nm.graph.community.GirvanNewman.EdgeBetweenessCtor
Construct an EdgeBetweeness from an undirected graph.
getEdges(Graph<V, ?>, V, V) - Static method in class dev.nm.graph.GraphUtils
Gets the set of edges that connect the two vertices.
getEigenvalue(int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Get the i-th eigenvalue.
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
 
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.DQDS
Gets the eigenvalues of the matrix, sorted in descending order.
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS
Gets all the eigenvalues of the symmetric tridiagonal matrix, sorted in descending order.
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
 
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Gets all the eigenvalues in descending order.
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
 
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
 
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Get all the eigenvalues.
getEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Get all the eigenvalues.
getEigenvalues() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Spectrum
Get all the eigenvalues.
getEigenvalues() - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the set of real eigenvalues.
getEigenvector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getEigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
Get an eigenvector.
getEigenVector(Vector, int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
Get an eigenvector from an initial guess.
getEigenvectorMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Gets the eigenvector matrix, each column is an eigenvector.
getEigenvectorMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
 
getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Gets all the eigenvectors which corresponds to the list of eigenvalues.
getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
 
getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
Gets the eigenvectors.
getEigenvectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Gets the eigenvectors of A, which are the columns of Q.
getEigenVectors() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
 
getEndIndex() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
Get the index of the last element of this cluster.
getEndPoint1() - Method in class dev.nm.geometry.LineSegment
Get the first endpoint.
getEndPoint2() - Method in class dev.nm.geometry.LineSegment
Get the second endpoint.
getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
getEntryList() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
getEntryList() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
Exports the non-zero values in the matrix as a list of SparseMatrix.Entrys.
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
 
getEqualityConstraints() - Method in interface dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblem
Gets the equality constraints, hi(x) = 0
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
 
getEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
 
getEstimates() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
Get the empirical estimates.
getEstimators() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting
Calculate the LARS fitting estimators.
getEstimators(int) - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the estimators (estimated psi, loading matrix, degree of freedom, test statistics, p-value, etc) obtained from the factor analysis, given the maximum number of iterations.
getEstimators(Vector, int) - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the estimators (estimated psi, loading matrix, degree of freedom, test statistics, p-value, etc) obtained from the factor analysis, given the initial psi and the maximum number of iterations.
getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
 
getEvaluationPoints() - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
Get the evaluation points for the quadrature rule (\(x_i\)).
getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
 
getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
 
getEvaluationPoints() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
 
getEventCountPerPeriod() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the mean peak rate, or the average number of peaks per period.
getExceedenceCount() - Method in class dev.nm.stat.evt.cluster.Clusters
Get the number of observations that is greater than a given threshold.
getExceptions() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
Get all exceptions encountered during execution.
getExpectedContingencyTable(int[], int[]) - Static method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
Assume the null hypothesis of independence, we compute the expected frequency of each category.
getExtHeaders() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the extended headers, including the subject and time headers.
getExtHeadersString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the extended headers, including the subject and time headers.
getFamily() - Method in class dev.nm.stat.regression.linear.glm.GLMProblem
Get the exponential family distribution of the mean.
getFeasibleInitialPoint() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
Given a collection of linear greater-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints.
getFeasibleInitialPoint() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
Given a collection of linear less-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints.
getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
Given a collection of linear greater-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints.
getFeasibleInitialPoint(LinearEqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
Given a collection of linear less-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints.
getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Initialize the first population.
getFirstGeneration() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.FirstGeneration
Generate the initial pool of chromosomes.
getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
 
getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion
 
getFirstGeneration() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
The initial population is generated by putting a uniform mesh/grid/net over the entire region.
getFirstNonIntegralIndices(double[]) - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
Get the index of the first integral variable whose value is not an integer, violating the integral constraints.
getFirstNonZeroIndex() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getFitResult() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
Get the fitted parameter.
getFittedOU(double[]) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
Fit an OU process by using MLE.
getFittedOU(double[]) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
Fit an OU process by using least squares regression.
getFittedOU(double[], double) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFitting
Get the fitted OU process.
getFittedOU(double[], double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
 
getFittedOU(double[], double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
 
getFittedParameters() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Get the fitted parameters.
getFittedState(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the posterior expected state.
getFittedState(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the posterior expected state.
getFittedStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the posterior expected states.
getFittedStates() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the posterior expected states.
getFittedStateVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the posterior expected state variance.
getFittedStateVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the posterior expected state variance.
getFlags() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Gets the factor flags.
getFractional(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
Get the fractional part of a number.
getFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Get an empty filtration of the process.
getFt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
Get the filtration.
getFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE
Get an empty filtration of the process.
getGARCHModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHModel
Get the GARCH part of this model.
getGraph() - Method in interface dev.nm.graph.GraphUtils.GraphFactory
Creates an empty graph.
getGreaterThanConstraint(Vector, int) - Static method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
Construct a greater-than constraint for the branching greater-than subproblem.
getGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
Get the greater-than-or-equal-to constraints of the linear programming problem.
getGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Get the set of linear greater-than-or-equal-to constraints.
getHeaders() - Method in class dev.nm.misc.datastructure.MathTable
Gets the column names.
getHMM() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
 
getHomogeneousSoln() - Method in interface dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
Get the basis of the homogeneous solution for the linear system, Ax = b.
getHouseholderMatrices() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Gets the householder reflections used in the reflection.
getInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Get the i-th inactive index.
getInactiveIndices() - Method in class dev.nm.misc.algorithm.ActiveSet
Get all inactive indices.
getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
 
getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
 
getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
 
getIncrement(Vector, Vector) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
Get the increment fraction, αk.
getIndex(String) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
Gets the index (starts from 1) of the product with a given symbol.
getIndexFromColLabel(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Translates a column label to a column index.
getIndexFromRowLabel(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Translates a row label to a row index.
getIndices() - Method in class dev.nm.misc.datastructure.MathTable
Gets a copy of the row indices.
getInitialGuess() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the initial guess of the solution for the problem.
getInitialHessian(Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
Get the initial Hessian matrix.
getInitialHessian(Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
getInitialHessian(Vector, Vector, Vector) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
Get the initial Hessian matrix.
getInitialHessian(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
 
getInitials() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
Generate a set of initial points for optimization.
getInitials() - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
Generate a set of initial points for optimization.
getInitials(Vector...) - Method in class dev.nm.solver.multivariate.initialization.DefaultSimplex
Build a simplex of N+1 vertices from an initial point, where N is the dimension of the initial points.
getInitials(Vector...) - Method in interface dev.nm.solver.multivariate.initialization.InitialsFactory
Generate a set of initial points for optimization from the fewer than required points.
getInitials(Vector...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
 
getInitials(Vector...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
 
getIntegerIndices() - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
Get the indices of the integral variables.
getIntegerIndices() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
getIntegerIndices() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
getIntegralConstraint(int) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem
Get the integral domain of a particular integral variable.
getIntervalLength() - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
Get the clustering interval length.
getInverseNorm() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getIterates() - Method in class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
Get a list of all iterative states.
getKalmanGain(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the Kalman gain.
getKalmanGain(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the Kalman gain.
getLambda(Vector) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP
Computes the value of the objective function in eq.
getLastNonZeroIndex() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getLeftHouseholders() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets all the accumulated left Householders.
getLeftPreconditioner() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the left preconditioner.
getLessThanConstraint(Vector, int) - Static method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
Construct a less-than constraint for the branching less-than subproblem.
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
 
getLessThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblem
Gets the less-than-or-equal-to constraints, gi(x) ≤ 0
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.ConstrainedOptimProblemImpl1
 
getLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
 
getLHS() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
 
getLHS(double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
Gets the left hand side coefficient matrix of the Crank-Nicolson scheme.
getLHS(int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
 
getLicenseKey() - Static method in class dev.nm.misc.license.License
Gets the license key string of the current license.
getLicenseLocation() - Static method in class dev.nm.misc.license.License
Gets the location of the current loaded license.
getLinearModel(Object) - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel
 
getLinearModel(Object) - Method in interface dev.nm.stat.regression.linear.panel.PanelRegression
Gets the linear model for a particular subject/individual.
getLinearSpan(double...) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Deprecated.
Not supported yet.
getLmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getLocation() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Get the location parameter.
getLogLikelihoodFunction() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Get the log-likelihood function.
getLower() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
Get the lower bounds of the confidence intervals.
getLowerLevel(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
getLowerParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
getLowerRight() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
Gets the lower right corner of the deflation.
getMAModel() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
Get the MA model of the inverse representation.
getMarginal1() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
getMarginal2() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
getMaskB() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getMaskC() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getMaxIteration() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the specified maximum number of iterations.
getMaxIterations() - Method in exception dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
Get the maximum number of iterations.
getMaxIterations() - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
Get the maximum number of iterations for this iterative procedure.
getMaxIterations() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
 
getMaxIterations() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
 
getMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics
Get the mean of the empirical estimates for each barrier level.
getMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
Get the mean values.
getMeanForecast(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Calculates the k-step ahead forecast of X in ARMA model.
getMeanResidual() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
 
getMeanReturns(double[][]) - Static method in class tech.nmfin.returns.Returns
Computes a vector of mean returns of the input returns (one column for one asset).
getMeanReturns(Matrix) - Static method in class tech.nmfin.returns.Returns
Computes a vector of mean returns of the input returns (one column for one asset).
getMeans() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the (weighted) mean of the estimates for the generated barrier levels.
getMeans(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.MeanEstimator
 
getMeans(Matrix, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
 
getMessage() - Method in error dev.nm.misc.license.LicenseError
 
getMessage() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
Gather the stack traces for the thrown exceptions.
getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
Gets the minimum of all the eigenvalues of a matrix.
getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
Gets the minimum of all the eigenvalues of a matrix.
getMinEigenValue(Matrix, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
Gets the minimum of all the eigen values of a matrix.
getMm() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getModel() - Method in class dev.nm.stat.evt.timeseries.MARMASim
Get the MARMA model.
getModel() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Gets the constructed model.
getModel() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Get the fitted ARMA model.
getModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the fitted ARIMA model.
getModel() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit
Get the fitted GARCH model.
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
Maximize, for each state, the log-likelihood of the distribution with respect to the observations and current estimators.
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
 
getMStepParams(double[], Vector[]) - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
 
getNegCount() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getNeighbors(Graph<V, ?>, V) - Static method in class dev.nm.graph.GraphUtils
Gets the set of vertices which are connected to v via any edges in this graph.
getNewFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
 
getNewFt() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
Get an empty filtration of the process.
getNewFt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
 
getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE
 
getNewFt() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.discrete.DiscreteSDE
Get an empty filtration of the process.
getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE
 
getNewFt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
 
getNewPool(int) - Static method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Allocate space for a population pool.
getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Populate the next generation using the parent and children chromosome pools.
getNextGeneration(List<Chromosome>, List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
 
getNn() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getNonIntegralIndices(double[]) - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem
Check which elements in x do not satisfy the integral constraints.
getNonIntegralIndices(double[]) - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
 
getNonIntegralIndices(double[]) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
getNormalization() - Method in class dev.nm.analysis.function.polynomial.Polynomial
Get the normalized version of this polynomial so the leading coefficient is 1.
getNullHypothesis() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.AndersonDarling
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
 
getNullHypothesis() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
 
getNullHypothesis() - Method in class dev.nm.stat.test.HypothesisTest
Get a description of the null hypothesis.
getNullHypothesis() - Method in class dev.nm.stat.test.mean.OneWayANOVA
 
getNullHypothesis() - Method in class dev.nm.stat.test.mean.T
 
getNullHypothesis() - Method in class dev.nm.stat.test.rank.KruskalWallis
 
getNullHypothesis() - Method in class dev.nm.stat.test.rank.SiegelTukey
 
getNullHypothesis() - Method in class dev.nm.stat.test.rank.VanDerWaerden
 
getNullHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
 
getNullHypothesis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
 
getNullHypothesis() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
 
getNullHypothesis() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
 
getNullHypothesis() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
Deprecated.
 
getNullHypothesis() - Method in class dev.nm.stat.test.variance.Bartlett
 
getNullHypothesis() - Method in class dev.nm.stat.test.variance.BrownForsythe
 
getNullHypothesis() - Method in class dev.nm.stat.test.variance.F
 
getNullHypothesis() - Method in class dev.nm.stat.test.variance.Levene
 
getObsDimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the dimension of the observations.
getObsDimension() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the dimension of observations.
getObservationModel() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the observation model.
getObservationModel() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the observation model.
getObservations() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
Get the observations.
getObservations() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
Get the observations.
getObservations(int) - Method in class dev.nm.stat.test.timeseries.adf.table.ADFDistributionTable
Get the observations to compute an empirical distribution.
getObservations(HmmInnovation[], int) - Static method in class dev.nm.stat.markovchain.MCUtils
Get all observations that occur in a particular state.
getOffsetVectors(Vector, Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Given the reference vector v0, the delta dv, and the range [a, b], the offset vectors are: v0 + a * dv, v0 + (a + 1) * dv, ..., v0 + b * dv.
getOmega() - Method in interface dev.nm.solver.multivariate.minmax.MinMaxProblem
Get the list of omegas, the domain.
getOne() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Pick a chromosome for mutation/crossover.
getOne() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Pick a random chromosome from the population.
getOperation(double[], double[]) - Method in interface dev.nm.number.doublearray.CompositeDoubleArrayOperation.ImplementationChooser
Get an implementation based on the inputs.
getOptimalBlockLength(double[], PattonPolitisWhite2009ForObject.Type) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Computes the optimal of block length.
getOptimalBlockLength(Object[], PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Computes the optimal of block length.
getOptimalLag(double[]) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Finds the smallest lag \(\hat{m}\) such that the autocorrelation for lags \((\hat{m} + k),~k=1,\dots,K_N\) are all insignificant regarding to the critical value.
getOptimalLag(Object[], PattonPolitisWhite2009ForObject.AutoCorrelationForObject) - Static method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Finds the smallest lag \(\hat{m}\) such that the autocorrelation for lags \((\hat{m} + k),~k=1,\dots,K_N\) are all insignificant regarding to the critical value.
getOptimalPositions(int, int) - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
 
getOptimalRiskAversionCoefficient() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Gets the optimal risk aversion coefficient w.r.t.
getOptimalRiskAversionCoefficient(double, double, double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Gets the optimal risk aversion coefficient w.r.t.
getOptimalValue() - Method in class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch
 
getOptimalW(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
 
getOptimalW(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Solves w_eff = argmin {q * (w' Σ w) - w'r}.
getOptimalWeightForSetLambda(double) - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
 
getOptimalWeightForSetLambda(double) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
 
getOptimalWeightForTargetReturn(double) - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
 
getOptimalWeightForTargetReturn(double) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
 
getOptimalWeights() - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Gets the Markowitz optimal portfolio weights, for a given risk aversion coefficient.
getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
 
getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm
Computes the optimal weights for the products using returns.
getOptimalWeights(Matrix, Vector, PortfolioOptimizationAlgorithm.SymbolLookup, LocalDateTimeInterval) - Method in class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
 
getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.BFS
 
getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.BottomUp
 
getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.DFS
 
getOrderedNodes() - Method in interface dev.nm.graph.algorithm.traversal.GraphTraversal
Gets the list of visited nodes, in the order of being visited.
getOrderedNodes() - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
Gets the collection of visited nodes to build a spanning tree.
getOrderedNodes(Collection<V>) - Method in class dev.nm.graph.algorithm.traversal.BottomUp
Gets the list of visited nodes, in the order of being visited.
getOrthogonalVector() - Method in class dev.nm.algebra.linear.vector.doubles.operation.Projection
Get the orthogonal vector which is equal to v minus the projection of v on {wi}.
getPanelValuesByHeaders(List<PanelData.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the values from a panel data.
getPanelValuesByHeaders(List<PanelData.Row>, String[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the values from a panel data.
getPanelValuesByHeaders(List<PanelData.Row>, String[], PanelData.Transformation[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the (transformed) values from a panel data.
getPanelValuesByTime(List<PanelData.Row>) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the (transformed) values from a panel data.
getPanelValuesByTime(List<PanelData.Row>, String[], PanelData.Transformation[]) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the (transformed) values from a panel data.
getParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
 
getParams() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
Get the parameters, for each state, of the distribution.
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
 
getParams() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
 
getParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
Get the set of vertices that have an outgoing arc pointing to a vertex.
getParticularSolution(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.Solution
Get a particular solution for the linear system.
getPeakMean() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the average value of peaks.
getPeaks() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
Get the peaks found in the observations.
getPenaltyFunction(ConstrainedOptimProblem) - Method in interface dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer.PenaltyFunctionFactory
Get an instance of the penalty function.
getPivot(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
 
getPivot(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
Get the next pivot.
getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials
 
getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
 
getPolynomial(int) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
 
getPolynomial(int) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.OrthogonalPolynomialFamily
Return an instance of the polynomial class of a given order.
getPopulation() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Get the current generation.
getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Gets the portfolio constraints represented in the objective function.
getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Gets the portfolio constraints.
getPortfolioConstraints() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Gets the portfolio constraints.
getPortfolioReturns(Vector, Vector) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
Computes the expected portfolio return.
getPortfolioVariance(Vector, Matrix) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
Computes the portfolio variance.
getPossiblePairs(int) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
getPossiblePairs(List<String>, List<String>) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
 
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
 
getPrecision() - Method in interface dev.nm.analysis.integration.univariate.riemann.Integrator
Get the convergence threshold.
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
 
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
 
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
 
getPrecision() - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
 
getPredictedObservation(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior observation prediction.
getPredictedObservation(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior observation prediction.
getPredictedObservations() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior observation predictions.
getPredictedObservations() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior observation predictions.
getPredictedObservationVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior observation prediction variance.
getPredictedObservationVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior observation prediction variance.
getPredictedState(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior expected state.
getPredictedState(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior expected state.
getPredictedStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior expected states.
getPredictedStates() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior expected states.
getPredictedStateVariance(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get the prior expected state variance.
getPredictedStateVariance(int) - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get the prior expected state variance.
getPriceMatrix(Vector, Vector) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
getPricesFromReturns(double, double[], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
Gets the price series from a return series.
getProblem() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the linear regression problem.
getProblemSize() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Get the number of variables in the problem or the cost/objective function.
getProcess() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Get the underlying OU process of this generator.
getProjectionLength(int) - Method in class dev.nm.algebra.linear.vector.doubles.operation.Projection
Get the length of v projected on each dimension {wi}.
getProjectionVector(int) - Method in class dev.nm.algebra.linear.vector.doubles.operation.Projection
Get the i-th projected vector of v on {wi}.
getProperty(int) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Get the i-th EigenProperty.
getProperty(Number) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Get the EigenProperty by eigenvalue.
getRandom() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Get the estimated seasonal effect of the time series.
getRealEigenvalues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Get all real eigenvalues.
getRealRoots(List<? extends Number>) - Static method in class dev.nm.analysis.function.polynomial.root.PolyRoot
Get a copy of only the real roots of a polynomial.
getReason() - Method in exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure
Get the reason for the convergence failure.
getReciprocal() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the reciprocal state switching GBM.
getReducedLinearEqualityConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
Deprecated.
Not supported yet.
getRegime(Matrix) - Method in class tech.nmfin.signal.infantino2010.Infantino2010Regime
Gets the current regime.
getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
 
getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory
 
getResamplerModel(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
 
getResidual() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getResultantTableau() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
 
getResultantTableau() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPSimplexMinimizer
Get the solution simplex table as a result of solving a linear programming problem.
getResultantTableau() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
 
getResults() - Method in exception dev.nm.misc.parallel.MultipleExecutionException
Get the results obtained so far.
getResults(List<D>) - Method in class dev.nm.misc.algorithm.BruteForce
Runs in parallel the brute force algorithm on the function for a given domain.
getResultsSerially(List<D>) - Method in class dev.nm.misc.algorithm.BruteForce
Deprecated.
getReturnLevel() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
Get the return level function for the estimated ACER function.
getReturns(double[], double[], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
 
getReturnsFromPrices(double[], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
Computes returns series from prices.
getRHS(int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
 
getRHS(Vector, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D.Coefficients
Computes the right hand side vector of the Crank-Nicolson scheme.
getRHS(Vector, double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D.Coefficients
 
getRightHouseholders() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets all the accumulated right Householders.
getRightPreconditioner() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the right preconditioner.
getRiskAversionCoefficientForTargetReturn(double, double, double, int) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
 
getRiskAversionCoefficientForTargetVariance(double, double, double, int) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
 
getRNG(T) - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
 
getRoot(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.DefaultRoot
 
getRoot(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.Diagonalization
 
getRoot(Matrix) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma.MatrixRoot
Gets the root of a matrix
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
getRow(int) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
Get the specified row in the matrix as a vector.
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Get a row.
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
getRow(int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
getRow(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
 
getRow(int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Gets a sub-row of the i-th row, from beginCol column to endCol column, inclusively.
getRow(S, T) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets one particular row indexed by a pair of subject and time.
getRowLabel(int) - Method in class dev.nm.misc.datastructure.FlexibleTable
Gets the label for row i.
getRowLabel(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
getRowOnOrAfter(double) - Method in class dev.nm.misc.datastructure.MathTable
Gets the row corresponding to a row index.
getRowOnOrBefore(double) - Method in class dev.nm.misc.datastructure.MathTable
Gets the row corresponding to a row index.
getRows() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets all of the panel data.
getRowsForSubject(S) - Method in class dev.nm.stat.regression.linear.panel.PanelData
Get all the rows pertaining to a particular subject.
getRowsOnOrAfter(double) - Method in class dev.nm.misc.datastructure.MathTable
Gets the rows having the row index value equal to or just bigger than i.
getRowsOnOrBefore(double) - Method in class dev.nm.misc.datastructure.MathTable
Gets the rows having the row index value equal to or just smaller than i.
getRQCorrection() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getRr() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getRSS() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
 
getScale() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Get the scale parameter.
getSeasonal() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Get the stationary random component of the time series after the trend and seasonal components are removed.
getSellThreshold() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Boundaries
Gets the sell threshold.
getShape() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Get the shape parameter.
getSharedInstance() - Static method in class dev.nm.misc.parallel.ParallelExecutor
Gets the globally shared executor.
getSharpeRatio(Vector, Vector, Matrix, double) - Static method in class tech.nmfin.portfoliooptimization.PortfolioUtils
Computes the portfolio Sharpe ratio.
getShift0() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getShift1() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getShiftB() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getShiftC() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getShrunkCovarianceMatrix() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Gets the nonlinear shrinkage covariance matrix.
getSignal(Matrix) - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin
 
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
Override this method to put in whatever constraints in the minimization problem.
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
 
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
 
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
 
getSimpleCell(RealScalarFunction, Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
Construct an instance of a SimpleCell.
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS
Gets all the singular values of the given bidiagonal matrix A, sorted in descending order.
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
 
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
 
getSingularValues() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
Get the normalized, hence positive, singular values.
getSingularValues() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Returns the singular values (same as the eigenvalues) of A.
getSolutionToOriginalProblem(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
Backs out the solution for the original (constrained) problem, if the modified (unconstrained) problem can be solved.
getSpanningCoefficients(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Find a linear combination of the basis that best approximates a vector in the least square sense.
getStandardError() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets the the auxiliary coefficients, Θ and V, in using the innovative algorithm.
getStateCounts(int[]) - Static method in class dev.nm.stat.markovchain.MCUtils
Count the numbers of occurrences of states.
getStateDimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the dimension of states.
getStateDimension() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the dimension of states.
getStateModel() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the state model.
getStateModel() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the state model.
getStates() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
Get the states.
getStates() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
Get the states.
getStationaryProbabilities(Matrix) - Static method in class dev.nm.stat.markovchain.SimpleMC
Gets the stationary state probabilities of a Markov chain that is irreducible, aperiodic and strongly connected (positive recurrent).
getStatistic() - Method in interface dev.nm.stat.descriptive.StatisticFactory
Get a Statistic.
getStats(CointegrationMLE) - Method in class dev.nm.stat.cointegration.JohansenTest
Get the set of likelihood ratio test statistics for testing H(r) in H(r+1).
getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoltzAnnealingFunction
 
getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.FastAnnealingFunction
 
getStepLength(double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
 
getSubProblem(ConstrainedOptimSubProblem) - Static method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
Gets the sub-problem in the form of ConstrainedOptimProblem.
getSymbol(int) - Method in interface tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SymbolLookup
Gets the symbol of the product at a given index (starts from 1).
getTable() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
Get a copy of the Neville table.
getTailedMatrix(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
getTailedMatrix(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
getThreshold() - Method in class dev.nm.stat.evt.cluster.ClusterAnalyzer
Get the threshold for exceedance.
getTime() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries.Entry
 
getTime() - Method in class dev.nm.stat.dlm.univariate.DLMSeries.Entry
 
getTime() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
 
getTime() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
 
getTime() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries.Entry
Get the timestamp.
getTime() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
 
getTolerance() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the specified Tolerance instance.
getTopN(Vector, int, double) - Static method in class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in interface tech.nmfin.meanreversion.cointegration.PairingModel
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel1
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel2
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel3
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel4
 
getTradingPairs(List<String>, List<String>, Matrix) - Method in class tech.nmfin.meanreversion.cointegration.PairingModel5
 
getTransformedMatrix() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets the final matrix transformed by all the Householder transformations.
getTransitionCounts(int[]) - Static method in class dev.nm.stat.markovchain.MCUtils
Count the numbers of times the state goes from one state to another.
getTrend() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Get the estimated trend of the time series.
getTurningPoints() - Method in interface tech.nmfin.portfoliooptimization.clm.MarkowitzCriticalLine
 
getTurningPoints() - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
 
getTwistIndex() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
 
getType() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
Gets the bi-diagonal matrix type.
getUmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getUpper() - Method in class dev.nm.stat.evt.evd.univariate.fitting.ConfidenceInterval
Get the upper bounds of the confidence intervals.
getUpperLeft() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Deflation
Gets the upper left corner of the deflation.
getUpperLevel(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
getUpperParameter() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
getValue() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries.Entry
 
getValue() - Method in class dev.nm.stat.dlm.univariate.DLMSeries.Entry
 
getValue() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
 
getValue() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
 
getValue() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries.Entry
Get the entry value.
getValue() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
 
getValue(String) - Static method in class dev.nm.misc.license.License
 
getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
getValueArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
getValueArray() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
Exports the non-zero values in the matrix as arrays of row/column indices and values.
getValueHeaders() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the headers, excluding the subject and time headers.
getValueHeadersString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Gets the headers, excluding the subject and time headers.
getVarForecast(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
Calculates the k-step ahead of conditional variance h in GARCH model.
getVariablePart(double[], Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction
Given an input to the original function, this extracts the variable parts (excluding the fixed values).
getVariables() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Gets the variables involved in the portfolio constraints implied by the objective function.
getVariables() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Get the list of all variables and their beginning indices starting from 0.
getVariables() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Get the list of all variables and their beginning indices starting from 0.
getVariables(SOCPConstraints) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
Gets the variables involved in SOCPGeneralConstraints.
getVARMA() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Get the ARMA part of this ARIMA model, essentially ignoring the differencing.
getVARMAX() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the ARMAX part of this ARIMAX model, essentially ignoring the differencing.
getVarResidual() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit
 
Getvec - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
Computes the (scaled) r-th column of the inverse of the sub-matrix block of the tridiagonal matrix T = LDLT - λ I.
Getvec(LDDecomposition, double, int, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec.Getvec
Computes an FP vector of a singleton.
getVectorReturnsFromPrices(double[][], ReturnsCalculator) - Static method in class tech.nmfin.returns.Returns
Computes returns for a 2D array of prices (one column for one asset), with the given ReturnsCalculator.
getVersion() - Static method in class dev.nm.misc.license.License
Gets the version number.
getViterbiStates(double[]) - Method in class dev.nm.stat.hmm.Viterbi
Gets the most likely sequence of states using Viterbi algorithm (global decoding), given the observations and the underlying hidden Markov model.
getVr() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
 
getWeightForObservation(double, double) - Method in interface dev.nm.analysis.curvefit.LeastSquares.Weighting
Specify the weight given to a particular observation.
getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
 
getWeighting(double) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
Get the weighting \(w(x_i)\) associated with a point \(x_i\).
getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
 
getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
 
getWeighting(double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
 
getWeights() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit.Result
 
getWhole(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
Get the integral part of a number (discarding the fractional part).
getWmask() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getWw() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
getX2() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
Get the Chi-squared distribution.
getX2() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
 
GEVFittingByMaximumLikelihood - Class in dev.nm.stat.evt.evd.univariate.fitting
Estimate the GeneralizedEVD parameter from the observations by maximum likelihood approach.
GEVFittingByMaximumLikelihood() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.GEVFittingByMaximumLikelihood
 
GirvanNewman<V,​E extends UndirectedEdge<V>,​G extends UnDiGraph<V,​E>> - Class in dev.nm.graph.community
The Girvan–Newman algorithm detects communities in complex systems.
GirvanNewman(UnDiGraph<V, E>, GirvanNewman.EdgeBetweenessCtor<V>, GraphUtils.GraphFactory<G>) - Constructor for class dev.nm.graph.community.GirvanNewman
Construct an instance of the Girvan-Newman algorithm.
GirvanNewman.EdgeBetweenessCtor<V> - Interface in dev.nm.graph.community
This allows customization of the computation of edge-betweeness.
GirvanNewmanUnDiGraph<V,​E extends UndirectedEdge<V>> - Class in dev.nm.graph.community
 
GirvanNewmanUnDiGraph(UnDiGraph<V, E>) - Constructor for class dev.nm.graph.community.GirvanNewmanUnDiGraph
Construct an instance of the Girvan-Newman algorithm.
GivensMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype
Givens rotation is a rotation in the plane spanned by two coordinates axes.
GivensMatrix(int, int, int, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Constructs a Givens matrix in the form \[ G(i,j,c,s) = \begin{bmatrix} 1 & ...
GivensMatrix(GivensMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Copy constructor.
gk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
the gradient at the k-th iteration
Glejser - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
The Glejser test tests for conditional heteroskedasticity.
Glejser(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.Glejser
Perform the Glejser test to test for heteroskedasticity in a linear regression model.
GLMBeta - Class in dev.nm.stat.regression.linear.glm
This is the estimate of beta, β^, in a Generalized Linear Model.
GLMBeta(GLMFitting, GLMResiduals) - Constructor for class dev.nm.stat.regression.linear.glm.GLMBeta
Construct an instance of Beta.
GLMBinomial - Class in dev.nm.stat.regression.linear.glm.distribution
This is the Binomial distribution of the error distribution in GLM model.
GLMBinomial() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
GLMExponentialDistribution - Interface in dev.nm.stat.regression.linear.glm.distribution
This interface represents a probability distribution from the exponential family.
GLMFamily - Class in dev.nm.stat.regression.linear.glm.distribution
Family provides a convenient way to specify the error distribution and link function used in GLM model.
GLMFamily(GLMBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct a Binomial family.
GLMFamily(GLMExponentialDistribution, LinkFunction) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct an instance of Family.
GLMFamily(GLMGamma) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct a Gamma family.
GLMFamily(GLMGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct a Gaussian family.
GLMFamily(GLMInverseGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct an Inverse Gaussian family.
GLMFamily(GLMPoisson) - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Construct a Poisson family.
GLMFitting - Interface in dev.nm.stat.regression.linear.glm
This interface represents a fitting method for estimating β in a Generalized Linear Model (GLM).
GLMGamma - Class in dev.nm.stat.regression.linear.glm.distribution
This is the Gamma distribution of the error distribution in GLM model.
GLMGamma() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
GLMGaussian - Class in dev.nm.stat.regression.linear.glm.distribution
This is the Gaussian distribution of the error distribution in GLM model.
GLMGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
GLMInverseGaussian - Class in dev.nm.stat.regression.linear.glm.distribution
This is the Inverse Gaussian distribution of the error distribution in GLM model.
GLMInverseGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
GLMModelSelection - Class in dev.nm.stat.regression.linear.glm.modelselection
Given a set of observations {y, X}, we would like to construct a GLM to explain the data.
GLMModelSelection(GLMProblem) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Constructs automatically a GLM model to explain the observations.
GLMModelSelection.ModelNotFound - Exception in dev.nm.stat.regression.linear.glm.modelselection
Throw a ModelNotFound exception when fail to construct a model to explain the data.
GLMPoisson - Class in dev.nm.stat.regression.linear.glm.distribution
This is the Poisson distribution of the error distribution in GLM model.
GLMPoisson() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
GLMProblem - Class in dev.nm.stat.regression.linear.glm
This is a Generalized Linear regression problem.
GLMProblem(Vector, Matrix, boolean, GLMFamily) - Constructor for class dev.nm.stat.regression.linear.glm.GLMProblem
Construct a GLM problem.
GLMProblem(LMProblem, GLMFamily) - Constructor for class dev.nm.stat.regression.linear.glm.GLMProblem
Construct a GLM problem from a linear regression problem.
GLMResiduals - Class in dev.nm.stat.regression.linear.glm
Residual analysis of the results of a Generalized Linear Model regression.
GLMResiduals(GLMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.glm.GLMResiduals
Performs residual analysis for a GLM regression.
GlobalSearchByLocalMinimizer - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
This minimizer is a global optimization method.
GlobalSearchByLocalMinimizer() - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
GlobalSearchByLocalMinimizer(LocalSearchCellFactory.MinimizerFactory, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
GlobalSearchByLocalMinimizer(RandomLongGenerator, double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.GlobalSearchByLocalMinimizer
Construct a GlobalSearchByLocalMinimizer to solve unconstrained minimization problems.
GOLDEN_RATIO - Static variable in class dev.nm.misc.Constants
the Golden ratio
GoldenMinimizer - Class in dev.nm.root.univariate.bracketsearch
This is the golden section univariate minimization algorithm.
GoldenMinimizer(double, int) - Constructor for class dev.nm.root.univariate.bracketsearch.GoldenMinimizer
Construct a univariate minimizer using the Golden method.
GoldenMinimizer.Solution - Class in dev.nm.root.univariate.bracketsearch
This is the solution to a Golden section univariate optimization.
GoldfeldQuandtTrotter - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
Goldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.
GoldfeldQuandtTrotter(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.GoldfeldQuandtTrotter
Constructs a symmetric, positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.
GOLUB_KAHAN - dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
Golub-Kahan, for higher precision.
GolubKahanSVD - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd
Golub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages.
GolubKahanSVD(Matrix, boolean, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
Run the Golub-Kahan SVD decomposition on a tall matrix.
GolubKahanSVD(Matrix, boolean, boolean, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
Runs the Golub-Kahan SVD decomposition on a tall matrix.
GomoryMixedCutMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
This cutting-plane implementation uses Gomory's mixed cut method.
GomoryMixedCutMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer
Construct a Gomory mixed cutting-plane minimizer to solve an MILP problem.
GomoryMixedCutMinimizer.MyCutter - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
This is Gomory's mixed cut.
GomoryPureCutMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
This cutting-plane implementation uses Gomory's pure cut method for pure integer programming, in which all variables are integral.
GomoryPureCutMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer
Construct a Gomory pure cutting-plane minimizer to solve pure ILP problems, in which all variables are integral.
GomoryPureCutMinimizer.MyCutter - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
This is Gomory's pure cut.
gradient(T) - Method in interface dev.nm.solver.multivariate.minmax.MinMaxProblem
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.
Gradient - Class in dev.nm.analysis.differentiation.multivariate
The gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field, and of which the magnitude is the greatest rate of change.
Gradient(RealScalarFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Gradient
Construct the gradient vector for a multivariate function f at point x.
GradientFunction - Class in dev.nm.analysis.differentiation.multivariate
The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x.
GradientFunction(RealScalarFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.GradientFunction
Construct the gradient function of a real scalar function f.
GramSchmidt - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr
The Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.
GramSchmidt(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
Run the Gram-Schmidt process to orthogonalize a matrix.
GramSchmidt(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
Run the Gram-Schmidt process to orthogonalize a matrix.
Graph<V,​E extends HyperEdge<V>> - Interface in dev.nm.graph
A graph is a representation of a set of objects where some pairs of the objects are connected by links.
GraphTraversal<V> - Interface in dev.nm.graph.algorithm.traversal
A spanning tree T of a connected, undirected graph G is a tree composed of all the vertices and some (or perhaps all) of the edges of G.
GraphTraversal.Node<V> - Class in dev.nm.graph.algorithm.traversal
This is a node in a spanning tree.
GraphUtils - Class in dev.nm.graph
These are the utility functions to manipulate Graph.
GraphUtils.EdgeFactory<V,​N,​E extends Edge<N>,​X> - Interface in dev.nm.graph
This interface specifies how an edge is created for two nodes.
GraphUtils.GraphFactory<G> - Interface in dev.nm.graph
The factory to construct instances of the graph type.
GRAVITATIONAL_G - Static variable in class dev.nm.misc.PhysicalConstants
The Newtonian constant of gravitation \(G\) in meters cubed per kilogram per second squared (m3 kg-1 s-2).
GRAY - dev.nm.graph.algorithm.traversal.DFS.Node.Color
on the current path
GREATER - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
compute Dn+; check whether the cdf of a sample lies above the null hypothesis
GREATER - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
two-sample; one-sided
GreaterThanConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
The domain of an optimization problem may be restricted by greater-than or equal-to constraints.
grid(double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
 
GridSearchCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
Searches (by brute force) for the maximal point of C(η) among a grid of values.
GridSearchCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
Constructs a maximizer using the default grid size for even grid search.
GridSearchCetaMaximizer(double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
Constructs a maximizer with a given grid size for even grid search.
GridSearchCetaMaximizer(GridSearchMinimizer.GridDefinition) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
Constructs a maximizer with a user-defined grid.
GridSearchMinimizer - Class in dev.nm.root.univariate
This performs a grid search to find the minimum of a univariate function.
GridSearchMinimizer(double) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
Constructs an instance with a given grid size for even grid.
GridSearchMinimizer(int) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
Constructs an instance with a given number of grid points for even grid.
GridSearchMinimizer(GridSearchMinimizer.GridDefinition) - Constructor for class dev.nm.root.univariate.GridSearchMinimizer
Constructs an instance with a user-defined grid.
GridSearchMinimizer.GridDefinition - Interface in dev.nm.root.univariate
 
GridSearchMinimizer.Solution - Class in dev.nm.root.univariate
This is the solution to the GridSearchMinimizer.
groupCount() - Method in class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
Returns the total number of independent groups.
GroupResampler - Class in dev.nm.stat.random.sampler.resampler.multivariate
 
GroupResampler(Matrix) - Constructor for class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
Constructs a re-sampler that treats each row as a group object, shuffling the groups/rows.
GroupResampler(Matrix, Resampler) - Constructor for class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
Constructs a re-sampler that treats each row as a group object, shuffling the groups/rows.
GroupResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
Creates re-samplers that do re-sampling for the whole group of stocks together.
GroupResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
 
GroupResamplerFactory(long) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
 
groups() - Method in class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
Returns the grouped variable indices.
Gs() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Gets the list of Gk's produced in the process of diagonalizing the tridiagonal matrix.
GSAAcceptanceProbabilityFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
The GSA acceptance probability function.
GSAAcceptanceProbabilityFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.GSAAcceptanceProbabilityFunction
Constructs a GSA acceptance probability function.
GSAAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
The GSA proposal/annealing function.
GSAAnnealingFunction(double, RandomLongGenerator, RandomStandardNormalGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
Constructs a GSA annealing function.
GSAMarkovLength(int) - Static method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
The Markov length for GSA, i.e.
GSATemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
The GSA temperature function.
GSATemperatureFunction(double, double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
Constructs a GSA temperature function.
GUMBEL - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Gumbel distribution.
GumbelDistribution - Class in dev.nm.stat.evt.evd.univariate
The Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, with \(\xi=0\).
GumbelDistribution() - Constructor for class dev.nm.stat.evt.evd.univariate.GumbelDistribution
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\).
GumbelDistribution(double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.GumbelDistribution
Create an instance with the given parameter values.

H

h() - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
Get the discretization size for the current iteration.
h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
 
h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
 
H - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
H() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
Gets the H matrix.
H() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Get the Householder matrix H = I - 2 * v * v'.
H() - Method in interface dev.nm.analysis.differentiation.differentiability.C2
Get the Hessian matrix function, H, of a real valued function f.
H() - Method in class dev.nm.solver.problem.C2OptimProblemImpl
 
H() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
H() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
 
H(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Gets H(t), the covariance matrix of ut.
H(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Get H(t), the variance of ut.
H(LeapFrogging.DynamicsState, RealScalarFunction, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
Evaluates a system's total energy at a given state.
Hadi() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
Hadi's influence measure.
HalleyRoot - Class in dev.nm.analysis.root.univariate
Halley's method is an iterative root finding method for a univariate function with a continuous second derivative, i.e., a C2 function.
HalleyRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.HalleyRoot
Construct an instance of Halley's root finding algorithm.
HarveyGodfrey - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
The Harvey-Godfrey test tests for conditional heteroskedasticity.
HarveyGodfrey(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.HarveyGodfrey
Perform the Harvey-Godfrey test to test for heteroskedasticity in a linear regression model.
hasDuplicate(double[], double) - Static method in class dev.nm.number.DoubleUtils
Check if a double array contains any duplicates.
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
hashCode() - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
hashCode() - Method in class dev.nm.analysis.function.tuple.Pair
 
hashCode() - Method in class dev.nm.interval.Interval
 
hashCode() - Method in class dev.nm.interval.Intervals
 
hashCode() - Method in class dev.nm.misc.datastructure.FlexibleTable
 
hashCode() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
hashCode() - Method in class dev.nm.misc.datastructure.SortableArray
 
hashCode() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
hashCode() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
hashCode() - Method in class dev.nm.number.complex.Complex
 
hashCode() - Method in class dev.nm.number.Real
 
hashCode() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
 
hasNext() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
 
hasZero(double[], double) - Static method in class dev.nm.number.DoubleUtils
Check if a double array has any 0.
hav(double) - Static method in class dev.nm.geometry.TrigMath
Returns the haversed sine or haversine of an angle.
HConstruction<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility
A construction of extreme and trade points based on H discretization, ignoring changes smaller than H.
head() - Method in interface dev.nm.graph.Arc
 
head() - Method in class dev.nm.graph.type.SimpleArc
 
header() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation
Gets the name of the column to apply the transformation to.
HeatEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
A one-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.
HeatEquation1D(double, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Constructs a heat equation problem.
HeatEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
A two-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.
HeatEquation2D(double, double, double, double, BivariateRealFunction, TrivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Constructs a two-dimensional heat equation problem.
height() - Method in interface dev.nm.graph.RootedTree
Gets the maximum depth in this tree.
height() - Method in class dev.nm.graph.type.SparseTree
 
height() - Method in class dev.nm.graph.type.VertexTree
 
HermitePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
A Hermite polynomial is defined by the recurrence relation below.
HermitePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials
 
HermiteRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
 
HermiteRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
Create a Hermite rule of the given order.
Hessenberg - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
An upper Hessenberg matrix is a square matrix which has zero entries below the first sub-diagonal.
Hessenberg() - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
Construct a Hessenberg utility class with the default deflation criterion.
Hessenberg(DeflationCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
Construct a Hessenberg utility class.
HessenbergDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.
HessenbergDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
Runs the Hessenberg decomposition for a square matrix.
HessenbergDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Given a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix.
HessenbergDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
 
HessenbergDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch
 
Hessian - Class in dev.nm.analysis.differentiation.multivariate
The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.
Hessian(RealScalarFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Hessian
Construct the Hessian matrix for a multivariate function f at point x.
Hessian() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
 
HessianFunction - Class in dev.nm.analysis.differentiation.multivariate
The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.
HessianFunction(RealScalarFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.HessianFunction
Construct the Hessian function of a real scalar function f.
Heteroskedasticity - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
A heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors).
Heteroskedasticity(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
Construct a heteroskedasticity test.
hHat() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the projection matrix, H-hat.
HiddenMarkovModel - Class in dev.nm.stat.hmm
 
HiddenMarkovModel(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel
 
HiddenMarkovModel(HMMRNG) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel
 
HilbertMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype
A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)
HilbertMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
Constructs a Hilbert matrix.
HilbertSpace<H,​F extends Field<F> & Comparable<F>> - Interface in dev.nm.algebra.structure
A Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured.
HmmInnovation - Class in dev.nm.stat.hmm
An HMM innovation consists of a state and an observation in the state.
HmmInnovation(int, double) - Constructor for class dev.nm.stat.hmm.HmmInnovation
Construct an HMM innovation.
HMMRNG - Class in dev.nm.stat.hmm
In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.
HMMRNG(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HMMRNG
Constructs a hidden Markov model.
HMMRNG(HMMRNG) - Constructor for class dev.nm.stat.hmm.HMMRNG
Copy constructor.
holdingTime(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets a suggested holding time based on an OU process.
holdingTimeByThreshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets a suggested holding time based on an OU process.
HomogeneousPathFollowingMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
HomogeneousPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
HomogeneousPathFollowingMinimizer(double, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.
HomogeneousPathFollowingMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
This is the solution to a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.
HornerScheme - Class in dev.nm.analysis.function.polynomial
Horner scheme is an algorithm for the efficient evaluation of polynomials in monomial form.
HornerScheme(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.HornerScheme
Evaluate a polynomial at x.
Householder4SubVector - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
Faster implementation of Householder reflection for sub-vectors at a given index.
Householder4SubVector(int, int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
Householder4SubVector(int, HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
Householder4SubVector(int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
Householder4SubVector(int, Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
Householder4ZeroGenerator - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
Faster implementation of Householder reflection for zero generator vector.
Householder4ZeroGenerator(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
 
HouseholderContext - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
This is the context information about a Householder transformation.
HouseholderContext(Vector, double, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
Constructs a Householder context information.
HouseholderInPlace - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
Maintains the matrix to be transformed by a sequence of Householder reflections.
HouseholderInPlace(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Creates an instance that transforms an identity matrix with the given dimension.
HouseholderInPlace(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Creates an instance that transforms the given matrix A.
HouseholderInPlace(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Creates an instance that transforms the given matrix A.
HouseholderInPlace.Householder - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
 
HouseholderQR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr
Successive Householder reflections gradually transform a matrix A to the upper triangular form.
HouseholderQR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Runs the Householder reflection process to orthogonalize a matrix.
HouseholderQR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Runs the Householder reflection process to orthogonalize a matrix.
HouseholderReflection - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder
A Householder transformation in the 3-dimensional space is the reflection of a vector in the plane.
HouseholderReflection(HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
 
HouseholderReflection(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Construct a Householder matrix from the vector that defines the hyperplane orthogonal to the vector.
HouseholderReflection(Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
 
Hp - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
This is the symmetrization operator as defined in equation (6) in the reference.
Hp() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
Constructs the symmetrization operator using an identity matrix.
Hp(Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp
Constructs a symmetrization operator.
HuangImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer.HuangImpl
 
HuangMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
Huang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas.
HuangMinimizer(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer
Construct a multivariate minimizer using Huang's method.
HuangMinimizer.HuangImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
an implementation of Huang's formula.
HybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
This class implements a hybrid MCMC algorithm.
HybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
Constructs a new instance with the given parameters.
HybridMCMCProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
 
HybridMCMCProposalFunction(Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
Constructs a hybrid MC proposal function.
HyperEdge<V> - Interface in dev.nm.graph
A hyper-edge connects a set of vertices of any size.
HypersphereRVG - Class in dev.nm.stat.random.rng.multivariate
Generates uniformly distributed points on the surface of a hypersphere.
HypersphereRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG
Constructs a hypersphere RVG to generate random uniform points.
HypersphereRVG(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG
Constructs a hypersphere RVG to generate random uniform points.
HypothesisTest - Class in dev.nm.stat.test
A statistical hypothesis test is a method of making decisions using experimental data.
HypothesisTest(double[]...) - Constructor for class dev.nm.stat.test.HypothesisTest
Construct an instance of HypothesisTest from the samples.

I

i - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
the row index
i() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets the value of i.
I - Static variable in class dev.nm.number.complex.Complex
a number representing 0.0 + 1.0i, the square root of -1
I - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
identity(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a new identity matrix.
IdentityHashSet<T> - Class in dev.nm.misc.datastructure
This class implements the Set interface with a hash table, using reference-equality in place of object-equality when comparing keys and values.
IdentityHashSet() - Constructor for class dev.nm.misc.datastructure.IdentityHashSet
Construct an empty IdentityHashSet.
IdentityHashSet(Collection<T>) - Constructor for class dev.nm.misc.datastructure.IdentityHashSet
Construct an IdentityHashSet with a collection of items.
IdentityPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
This identity preconditioner is used when no preconditioning is applied.
IdentityPreconditioner() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
 
ifelse(double[], DoubleUtils.ifelse) - Static method in class dev.nm.number.DoubleUtils
Return a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is true or false.
IID - Class in dev.nm.stat.random.rng.multivariate
An i.i.d.
IID(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.IID
Construct a rvg that outputs vectors that have i.i.d.
ILPBranchAndBoundMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.bb
This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.
ILPBranchAndBoundMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
ILPBranchAndBoundMinimizer(ILPBranchAndBoundMinimizer.ActiveListFactory) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.
ILPBranchAndBoundMinimizer.ActiveListFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.bb
This factory constructs a new instance of ActiveList for each Integer Linear Programming problem.
ILPNode - Class in dev.nm.solver.multivariate.constrained.integer.linear.bb
This is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem.
ILPNode(ILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
Construct a BB node and associate it with an ILP problem.
ILPProblem - Interface in dev.nm.solver.multivariate.constrained.integer.linear.problem
A linear program in real variables is said to be integral if it has at least one optimal solution which is integral.
ILPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.integer.linear.problem
This implementation is an ILP problem, in which the variables can be real or integral.
ILPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, int[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
Construct an ILP problem, in which the variables can be real or integral.
imaginary() - Method in class dev.nm.number.complex.Complex
Get the imaginary part of this complex number.
ImmutableMatrix - Class in dev.nm.algebra.linear.matrix.doubles
This is a read-only view of a Matrix instance.
ImmutableMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
Construct a read-only version of a matrix.
ImmutableVector - Class in dev.nm.algebra.linear.vector.doubles
This is a read-only view of a Vector instance.
ImmutableVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.ImmutableVector
Construct a read-only version of a vector.
ImplicitModelPCA - Class in dev.nm.stat.factor.implicitmodelpca
Given a (de-meaned) time series of vectored observations, we decompose them into a reduced dimension of linear sum of implicit factors.
ImplicitModelPCA(Matrix) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
Constructs an implicit-model that will have one and only one implicit factors.
ImplicitModelPCA(Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
Constructs an implicit-model that will have the number of implicit factors such that the variance explained is bigger than a threshold
ImplicitModelPCA(Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
Constructs an implicit-model that will have K implicit factors.
ImplicitModelPCA.Result - Class in dev.nm.stat.factor.implicitmodelpca
the regression results
ImportanceSampling - Class in dev.nm.stat.random.variancereduction
Importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than the distribution of interest.
ImportanceSampling(UnivariateRealFunction, UnivariateRealFunction, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.ImportanceSampling
Uses importance sample to do Monte Carlo integration.
IN_EXACT_LINE_SEARCH - dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
The line search is done using Fletcher's inexact line search method, FletcherLineSearch.
inactiveSize() - Method in class dev.nm.misc.algorithm.ActiveSet
Get the number of inactive indices.
incomingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
incomingArcs(V) - Method in interface dev.nm.graph.DiGraph
Gets the set of all incoming arcs associated with a vertex in this graph.
incomingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph
 
incomingArcs(V) - Method in class dev.nm.graph.type.SparseTree
 
IndependentCoVAR - Class in tech.nmfin.meanreversion.daspremont2008
This algorithm finds the independent variables based on the covariance matrix.
IndependentCoVAR(Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR
Runs the algorithm with the given covariance matrix.
index - Variable in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
the index to the variable, counting from 1.
index - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
the index of a variable, an inequality, an equality, etc., counting from 1
index - Variable in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
the index of an integral variable
index() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
Gets the index of this entry in the sparse vector, counting from 1.
Infantino2010PCA - Class in tech.nmfin.signal.infantino2010
The objective is to predict the next H-period accumulated returns from the past H-period dimensionally reduced returns.
Infantino2010PCA(int, int, int, int, boolean) - Constructor for class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
Infantino2010PCA.Signal - Class in tech.nmfin.signal.infantino2010
 
Infantino2010Regime - Class in tech.nmfin.signal.infantino2010
Detects the current regime (mean reversion or momentum) by cross-sectional volatility.
Infantino2010Regime(int) - Constructor for class tech.nmfin.signal.infantino2010.Infantino2010Regime
 
Infantino2010Regime.Regime - Enum in tech.nmfin.signal.infantino2010
 
informationCriteria() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
Gets the model selection criteria.
init(double, double) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
Initializes the algorithm states with initial \(x_{min}\) and \(f_{min}\) before iterations.
init(double, double) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
 
initialCondition() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
Initializes the grid with the initial conditions.
initialReturns - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
initials - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
InitialsFactory - Interface in dev.nm.solver.multivariate.initialization
Some optimization algorithms, e.g., Nelder-Mead, Differential-Evolution, require a set of initial points to work with.
initialSigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
innerProduct(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
innerProduct(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
innerProduct(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
innerProduct(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Inner product in the Euclidean space is the dot product.
innerProduct(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the inner or dot product of two vectors.
innerProduct(H) - Method in interface dev.nm.algebra.structure.HilbertSpace
<⋅,⋅> : H × H → F
InnerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.
InnerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct
Compute the inner product of two matrices.
InnovationsAlgorithm - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess
The innovations algorithm is an efficient way to obtain a one step least square linear predictor for a univariate linear time series with known auto-covariance and these properties (not limited to ARMA processes): {xt} can be non-stationary. E(xt) = 0 for all t. This class implements the part of the innovations algorithm that computes the prediction error variances, v and prediction coefficients θ.
InnovationsAlgorithm(int, AutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
Constructs an instance of InnovationsAlgorithm for a univariate time series with known auto-covariance structure.
instantPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
 
intArray2doubleArray(int...) - Static method in class dev.nm.number.DoubleUtils
Convert an int array to a double array.
intArray2List(int[]) - Static method in class dev.nm.number.DoubleUtils
Convert an int array to a list.
IntegerDomain(int, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
Construct the integral domain for an integral variable.
IntegerDomain(int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
Construct the integral domain for an integral variable.
IntegerDomain(int, int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain
Construct the integral domain for an integral variable.
Integral - Class in dev.nm.stat.stochasticprocess.univariate.integration
The class represents an integral of a function, in the Lebesgue sense.
Integral(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.Integral
Construct an integral from an integrand.
IntegralConstrainedCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.
IntegralConstrainedCellFactory(DEOptimCellFactory, IntegralConstrainedCellFactory.IntegerConstraint) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory
Construct an instance of IntegralConstrainedCellFactory.
IntegralConstrainedCellFactory.AllIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
This integral constraint makes all variables in the objective function integral variables.
IntegralConstrainedCellFactory.IntegerConstraint - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
The integral constraints are defined by implementing this interface.
IntegralConstrainedCellFactory.SomeIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
This integral constraint makes some variables in the objective function integral variables.
IntegralDB - Class in dev.nm.stat.stochasticprocess.univariate.integration
This class evaluates the following class of integrals.
IntegralDB(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB
Construct an integral for f with respect to dB.
IntegralDt - Class in dev.nm.stat.stochasticprocess.univariate.integration
This class evaluates the following class of integrals.
IntegralDt(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt
Construct an integral for f with respect to dt.
IntegralExpectation - Class in dev.nm.stat.stochasticprocess.univariate.integration
This class computes the expectation of the following class of integrals.
IntegralExpectation(Integral, double, double, int, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
Compute the expectation for the integral of a stochastic process.
IntegralExpectation(Integral, double, double, int, int, long) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
Compute the expectation for the integral of a stochastic process.
integrate(ODE1stOrder, double[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.ODEIntegrator
This is the integration method that approximates the solution of a first order ODE.
integrate(ODE1stOrder, double[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
 
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable
 
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature
 
integrate(UnivariateRealFunction, double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.Integrator
Integrate function f from a to b, \[ \int_a^b\! f(x)\, dx \]
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
 
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
 
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
 
integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
 
integrate(UnivariateRealFunction, double, double, SubstitutionRule) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann
Integrate a function, f, from a to b possibly using change of variable.
Integrator - Interface in dev.nm.analysis.integration.univariate.riemann
This defines the interface for the numerical integration of definite integrals of univariate functions.
intercept() - Method in class dev.nm.stat.regression.linear.LMProblem
Checks if an intercept term is added to the linear regression.
interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation
 
interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicSpline
 
interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BilinearInterpolation
 
interpolate(BivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGridInterpolation
Constructs a real valued function from a grid of observations.
interpolate(MultivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGridInterpolation
Construct a real valued function from a grid of observations.
interpolate(MultivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
 
Interpolation - Interface in dev.nm.analysis.curvefit.interpolation.univariate
Interpolation is a method of constructing new data points within the range of a discrete set of known data points.
Interval<T extends Comparable<? super T>> - Class in dev.nm.interval
For a partially ordered set, there is a binary relation, denoted as ≤, that indicates that, for certain pairs of elements in the set, one of the elements precedes the other.
Interval(T, T) - Constructor for class dev.nm.interval.Interval
Construct an interval.
IntervalRelation - Enum in dev.nm.interval
Allen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.
Intervals<T extends Comparable<? super T>> - Class in dev.nm.interval
This is a disjoint set of intervals.
Intervals() - Constructor for class dev.nm.interval.Intervals
Construct an empty set of intervals.
Intervals(Interval<T>) - Constructor for class dev.nm.interval.Intervals
Construct a set that contains only one interval.
Intervals(Interval<T>...) - Constructor for class dev.nm.interval.Intervals
Construct a set of intervals.
Intervals(Intervals<T>) - Constructor for class dev.nm.interval.Intervals
Copy constructor.
Intervals(T, T) - Constructor for class dev.nm.interval.Intervals
Construct a set that contains only one interval [begin, end].
IntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
This is a univariate time series indexed by integers.
IntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
This is the TimeSeries.Entry for an integer number -indexed univariate time series.
intValue() - Method in class dev.nm.number.complex.Complex
Deprecated.
Invalid operation.
intValue() - Method in class dev.nm.number.Real
 
intValue() - Method in class dev.nm.number.ScientificNotation
 
InvalidLicense - Error in dev.nm.misc.license
This is the LicenseError thrown when calling a class or method that is not yet licensed.
InvalidLicense(String) - Constructor for error dev.nm.misc.license.InvalidLicense
 
invdet() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix
One over the determinant of H: 1/|H|, which is an integer.
inverse() - Method in interface dev.nm.algebra.structure.Field
For each a in F, there exists an element b in F such that a × b = b × a = 1.
inverse() - Method in class dev.nm.number.complex.Complex
 
inverse() - Method in class dev.nm.number.Real
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
 
inverse(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.link.LinkFunction
Inverse of the link function, i.e., g-1(x).
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
Inverse of the link function, i.e., g-1(x).
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
 
inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
 
Inverse - Class in dev.nm.algebra.linear.matrix.doubles.operation
For a square matrix A, the inverse, A-1, if exists, satisfies A.multiply(A.inverse()) == A.ONE() There are multiple ways to compute the inverse of a matrix.
Inverse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Inverse
Constructs the inverse of a matrix.
Inverse(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Inverse
Constructs the inverse of a matrix.
INVERSE - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates
 
INVERSE_FINE_STRUCTURE_ALPHA1 - Static variable in class dev.nm.misc.PhysicalConstants
The inverse fine-structure constant \(\alpha^{-1}\) (dimensionless).
INVERSE_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the inverse of empirical distribution function
INVERSE_OF_EMPIRICAL_CDF_WITH_AVERAGING_AT_DISCONTINUITIES - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the inverse of empirical distribution function with averaging at discontinuities
inverseCovariance() - Method in interface dev.nm.stat.covariance.covarianceselection.CovarianceSelectionSolver
Get the estimated inverse Covariance matrix of the selection problem.
inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Gets the inverse of the estimated covariance matrix.
inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
Get the inverse of the estimated covariance matrix.
inverseCovariance() - Method in class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation
 
InverseIteration - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
Inverse iteration is an iterative eigenvalue algorithm.
InverseIteration(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
Construct an instance of InverseIteration to find the corresponding eigenvector.
InverseIteration(Matrix, double, InverseIteration.StoppingCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration
Construct an instance of InverseIteration to find the corresponding eigenvector.
InverseIteration.StoppingCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
This interface defines the convergence criterion.
InverseNonExistent() - Constructor for exception dev.nm.algebra.structure.Field.InverseNonExistent
Construct an instance of InverseNonExistent
InverseTransformSampling - Class in dev.nm.stat.random.rng.univariate
Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.) is a basic method for pseudo-random number sampling, i.e.
InverseTransformSampling(ProbabilityDistribution) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling
Construct a random number generator to sample from a distribution.
InverseTransformSampling(ProbabilityDistribution, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling
Construct a random number generator to sample from a distribution.
InverseTransformSamplingEVDRNG - Class in dev.nm.stat.evt.evd.univariate.rng
Generate random numbers according to a given univariate extreme value distribution, by inverse transform sampling.
InverseTransformSamplingEVDRNG(UnivariateEVD) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
Create an instance with the given extreme value distribution.
InverseTransformSamplingEVDRNG(UnivariateEVD, long...) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
Create an instance with the given extreme value distribution and seeds for this random number generator.
InverseTransformSamplingExpRNG - Class in dev.nm.stat.random.rng.univariate.exp
This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.
InverseTransformSamplingExpRNG() - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG
Constructs a random number generator to sample from the standard exponential distribution using the inverse transform sampling method.
InverseTransformSamplingExpRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG
Constructs a random number generator to sample from the exponential distribution using the inverse transform sampling method.
InverseTransformSamplingGammaRNG - Class in dev.nm.stat.random.rng.univariate.gamma
Deprecated.
There exist much more efficient algorithms.
InverseTransformSamplingGammaRNG() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG
Deprecated.
Construct a random number generator to sample from the standard gamma distribution using the inverse transform sampling method.
InverseTransformSamplingGammaRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG
Deprecated.
Construct a random number generator to sample from the gamma distribution using the inverse transform sampling method.
InverseTransformSamplingTruncatedNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal.truncated
A random variate x defined as \[ x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu \] with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a uniform random number on (0, 1), follows the distribution truncated to the range (a, b).
InverseTransformSamplingTruncatedNormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
Construct a rng that samples from a truncated standard Normal distribution using inverse sampling technique.
InverseTransformSamplingTruncatedNormalRNG(double, double, double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.
InverseTransformSamplingTruncatedNormalRNG(double, double, double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.
InvertingVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This is the inverting-variable transformation.
InvertingVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
Construct an InvertingVariable substitution rule.
invOfwAtwA() - Method in class dev.nm.stat.regression.linear.LMProblem
(wA' * wA)-1
IPMinimizer<T extends IPProblem,​S extends MinimizationSolution<Vector>> - Interface in dev.nm.solver.multivariate.constrained.integer
An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints.
IPProblem - Interface in dev.nm.solver.multivariate.constrained.integer
An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.
IPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.integer
This is an implementation of a general Integer Programming problem in which some variables take only integers.
IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
Construct a constrained optimization problem with integral constraints.
IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
Construct a constrained optimization problem with integral constraints.
ir() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
 
is(IntervalRelation, Interval<T>) - Method in class dev.nm.interval.Interval
Check whether this and Y satisfies a certain Allen's interval relation.
isAcyclic() - Method in class dev.nm.graph.type.SparseDAGraph
Runs validity check to ensure that this DA graph is indeed acyclic.
isAcyclic(UnDiGraph<V, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
Check if an undirected graph is acyclic.
isAllZeros(double[], double) - Static method in class dev.nm.number.DoubleUtils
Check if a double array contains only 0s, entry-by-entry.
isArray(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is a row or a column.
isBalanced() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Checks if the panel is balanced, i.e., all subjects have the same number of observations (times).
isBetween(Interval<T>, Interval<T>) - Method in enum dev.nm.interval.IntervalRelation
Check if X and Y satisfy a certain relation.
isBracketing(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
Check whether [xl, xu] is bracketing x.
isCandidate() - Method in interface dev.nm.misc.algorithm.bb.BBNode
Check if this node is a possible solution to the original problem, e.g., not pruned.
isCandidate() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
isColumn(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is a column.
isConnected(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
Check whether an undirected graph is connected.
isConverged() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
This is the convergence criterion.
isConverged() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
This genetic algorithm terminates if the minimum does not improve for a fixed number of iterations, or the maximum number of iterations is exceeded.
isConverged() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST
Checks if the algorithm converges.
isCyclic - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node
 
isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS
Checks if the graph is cyclic.
isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
Check whether this node is on a cyclic path of the graph.
isDiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a square matrix is a diagonal matrix, up to a precision.
isEmpty() - Method in class dev.nm.graph.community.EdgeBetweeness
Checks if there is no edge, e.g., all vertices are isolated.
isEmpty() - Method in interface dev.nm.misc.algorithm.bb.ActiveList
Returns true if this collection contains no elements.
isEmpty() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
isFat(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is fat.
isFeasible() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Check if this table is feasible.
isFixedIndex(int) - Method in class dev.nm.analysis.function.SubFunction
Checks whether a particular index corresponds a fixed variable/value.
isFixedIndex(int, Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction
Checks whether a particular index corresponds a fixed variable/value.
isFree(int) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem
Check whether xi is a free variable after handling the box constraints.
isFree(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
isFree(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
isFree(int) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
 
isHessenberg(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
Check if H is upper Hessenberg.
isIdempotent(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is idempotent.
isIdentity(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is an identity matrix, up to a precision.
isInBox(Vector, Vector, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Check if a solution is within a box.
isInfinite(Complex) - Static method in class dev.nm.number.complex.Complex
Check if a complex number is an infinity; i.e., either the real or the imaginary part is infinite, c.f., Double.isInfinite(), and the number is not a NaN.
isInKernel(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Deprecated.
Not supported yet.
isLASSO() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Checks if the LASSO variation of LARS is used.
isLowerBidiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is lower bidiagonal, up to a precision.
isLowerTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is lower triangular, up to a precision.
isMagicSquare(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Deprecated.
Not supported yet.
isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the convergence criterion
isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
the convergence criterion
isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
This algorithm stops only after a pre-specified number of iterations.
isMinFound() - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer.Solution
 
isNaN(Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a vector contains any NaN entry.
isNaN(Complex) - Static method in class dev.nm.number.complex.Complex
Check if a complex number is an NaN; i.e., either the real or the imaginary part is an NaN.
isNegative(double, double) - Static method in class dev.nm.number.DoubleUtils
Check if d is negative.
isNegligible(Matrix, int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion
Checks if H[i,j] is negligible by Steward's deflation criterion.
isNegligible(Matrix, int, int, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DeflationCriterion
Checks whether a sub-diagonal element is sufficiently small.
isNullOrEmpty(String) - Static method in class dev.nm.misc.StringUtils
Checks if a string is either null or empty.
isNullRejected(double) - Method in class dev.nm.stat.test.HypothesisTest
Use the p-value to check whether the null hypothesis can be rejected for a given significance level.
isNumber(double) - Static method in class dev.nm.number.DoubleUtils
Check if a double is a number, i.e., it is not &infin; or NaN.
isOK() - Method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
 
isOK() - Method in interface tech.nmfin.meanreversion.cointegration.check.PairingCheck
 
isOrthogonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is orthogonal, up to a precision.
isPositive(double, double) - Static method in class dev.nm.number.DoubleUtils
Check if d is positive.
isPositiveDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a square matrix is positive definite; the matrix needs not be symmetric.
isPositiveSemiDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a square matrix is positive definite, up to a precision.
isPow2(int) - Static method in class dev.nm.number.DoubleUtils
Check if an integer is a power of 2.
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
 
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
 
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
Decides whether the given proposed state should be accepted, or whether the system should remain in it's current state.
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
 
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
 
isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
 
isProposalAccepted(RealScalarFunction, RandomLongGenerator, Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisUtils
Uses the given LOG density function to determine whether the given state transition should be accepted.
isQuasiTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is quasi (upper) triangular, up to a precision.
isReal(Complex) - Static method in class dev.nm.number.complex.Complex
Check if this complex number is a real number; i.e., the imaginary part is 0.
isReal(Number) - Static method in class dev.nm.number.NumberUtils
Check if a number is a real number.
isReducedRowEchelonForm(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is in the reduced row echelon form, up to a precision.
isReducible() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
Check if we can reduce the number of linear equalities.
isReducible(Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
Check if H is upper Hessenberg and is reducible.
isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance
 
isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
 
isResidualSmall(double) - Method in interface dev.nm.misc.algorithm.iterative.tolerance.Tolerance
Checks if the updated residual satisfies the tolerance criteria.
isRow(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is a row.
isRowEchelonForm(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is in the row echelon form, up to a precision.
isSameDimension(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if two tables have the same dimension.
isSatisfied(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
Checks if the constraints are satisfied.
isSatisfied(Constraints, Vector, double) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils
Checks if the constraints are satisfied.
isScalar(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Deprecated.
Not supported yet.
isSelected(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Checks whether a particular indexed factor is selected in the model.
isSingular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a square matrix is singular, i.e, having no inverse, up to a precision.
isSkewSymmetric(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is skew symmetric.
isSpanned(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Check whether a vector is in the span of the basis.
isSquare(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is square.
isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterIterations
 
isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
 
isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AndStopConditions
 
isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AtThreshold
 
isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.OrStopConditions
 
isStopped(Vector, double...) - Method in interface dev.nm.misc.algorithm.stopcondition.StopCondition
This is called after each iteration to determine whether the termination conditions are met, e.g., convergence.
isStronglyConnected(DiGraph<V, ? extends Arc<V>>) - Static method in class dev.nm.graph.GraphUtils
Check whether a directed graph is strongly connected.
isSymmetric(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is symmetric.
isSymmetricPositiveDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a square matrix is symmetric and positive definite.
isTall(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Check if a table is tall.
isTridiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is tridiagonal, up to a precision.
isTrue(double, int) - Method in interface dev.nm.number.DoubleUtils.which
Decide whether x is to be selected.
isUnique() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution
Return true if the quadratic programming problem has only one solution.
isUnreduced(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
A bi-diagonal matrix is unreduced if it has no 0 on both the super and main diagonals.
isUpperBidiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is upper bidiagonal, up to a precision.
isUpperTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a matrix is upper triangular, up to a precision.
isValidated(Matrix) - Method in class dev.nm.stat.test.distribution.pearson.AS159
Checks whether a matrix satisfies the row and column sums.
isWeekend(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Checks if the given time is a weekend.
isZero() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Check if the kernel has zero dimension, that is, if A has full rank.
isZero(double, double) - Static method in class dev.nm.number.DoubleUtils
Check if d is zero.
isZero(Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if a vector is a zero vector, i.e., all its entries are 0, up to a precision.
iter - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the current iteration count
iter - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007
 
iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4_1a
 
iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4_1b
 
iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4
 
IteratesMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
This IterationMonitor stores all states generated during iterations.
IteratesMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor
Construct a monitor to keep track of the states in all iterations.
iteration - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
iteration(Elliott2005DLM, double[]) - Static method in class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
 
IterationBody<T> - Interface in dev.nm.misc.parallel
This interface defines the code snippet to be run in parallel.
IterationMonitor<S> - Interface in dev.nm.misc.algorithm.iterative.monitor
To debug an iterative algorithm, such as in IterativeMethod, it is useful to keep track of the all states generated in the iterations.
IterativeC2Maximizer - Class in dev.nm.solver.multivariate.unconstrained.c2
A maximization problem is simply minimizing the negative of the objective function.
IterativeC2Maximizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
Construct a multivariate maximizer to maximize an objective function.
IterativeC2Maximizer(T) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
Construct a multivariate maximizer to maximize an objective function.
IterativeC2Maximizer.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2
 
IterativeC2Minimizer - Interface in dev.nm.solver.multivariate.unconstrained.c2
This is a minimizer that minimizes a twice continuously differentiable, multivariate function.
IterativeCentralDifference - Class in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2
An iterative central difference algorithm to obtain a numerical approximation to Poisson's equations with Dirichlet boundary conditions.
IterativeCentralDifference(double, int) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference
Create an instance of this method with the given error bound as the convergence criterion, and the maximum number of iterations allowed.
IterativeIntegrator - Interface in dev.nm.analysis.integration.univariate.riemann
An iterative integrator computes an integral by a series of sums, which approximates the value of the integral.
IterativeLinearSystemSolver - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
An iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications.
IterativeLinearSystemSolver.Solution - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
This is the solution to a system of linear equations using an iterative solver.
IterativeMethod<S> - Interface in dev.nm.misc.algorithm.iterative
An iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems.
IterativeMinimizer<P extends OptimProblem> - Interface in dev.nm.solver.multivariate.unconstrained
This is an iterative multivariate minimizer.
IterativeSolution<S> - Interface in dev.nm.solver
Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution.
iterator() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
iterator() - Method in class dev.nm.combinatorics.Twiddle
 
iterator() - Method in class dev.nm.misc.algorithm.CartesianProduct
 
iterator() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
iterator() - Method in class dev.nm.misc.datastructure.MultiDimensionalGrid
 
iterator() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
 
iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
 
iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
 
iterator() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
 
iterator() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
 
iterator() - Method in class dev.nm.stat.random.rng.ConstantSeeder
 
iterator() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator
 
iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
 
iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
 
IWLS - Class in dev.nm.stat.regression.linear.glm
This implementation estimates parameters β in a GLM model using the Iteratively Re-weighted Least Squares algorithm.
IWLS(double, int) - Constructor for class dev.nm.stat.regression.linear.glm.IWLS
Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.

J

j - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
the column index
j() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets the value of j.
Jacobian - Class in dev.nm.analysis.differentiation.multivariate
The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.
Jacobian(RealScalarFunction[], Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
Construct the Jacobian matrix for a multivariate function f at point x.
Jacobian(RealVectorFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
Construct the Jacobian matrix for a multivariate function f at point x.
Jacobian(List<RealScalarFunction>, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian
Construct the Jacobian matrix for a multivariate function f at point x.
JacobianFunction - Class in dev.nm.analysis.differentiation.multivariate
The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.
JacobianFunction(RealVectorFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.JacobianFunction
Construct the Jacobian function of a real scalar function f.
JacobiPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
The Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal of the coefficient matrix, i.e., P = diag(A).
JacobiPreconditioner(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
Construct a Jacobi preconditioner.
JacobiSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
The Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration.
JacobiSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
Construct a Jacobi solver.
JarqueBera - Class in dev.nm.stat.test.distribution.normality
The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness.
JarqueBera(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera
Perform the Jarque-Bera test to test for the departure from normality, using the asymptotic chi-square distribution.
JarqueBera(double[], boolean) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera
Perform the Jarque-Bera test to test for the departure from normality.
JarqueBeraDistribution - Class in dev.nm.stat.test.distribution.normality
Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.
JarqueBeraDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution
Construct a Jarque-Bera distribution using Monte Carlo simulation.
JarqueBeraDistribution(int, int, StandardNormalRNG) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution
Construct a Jarque-Bera distribution using Monte Carlo simulation.
JenkinsTraubReal - Class in dev.nm.analysis.function.polynomial.root.jenkinstraub
The Jenkins-Traub algorithm is a fast globally convergent iterative method for solving for polynomial roots.
JenkinsTraubReal() - Constructor for class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
 
JohansenAsymptoticDistribution - Class in dev.nm.stat.cointegration
Johansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests), each for 5 different trend types.
JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
Construct the asymptotic distribution of a Johansen test.
JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
Construct the asymptotic distribution of a Johansen test.
JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int, long) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution
Construct the asymptotic distribution of a Johansen test.
JohansenAsymptoticDistribution.F - Interface in dev.nm.stat.cointegration
This is a filtration function.
JohansenAsymptoticDistribution.Test - Enum in dev.nm.stat.cointegration
the available types of Johansen cointegration tests
JohansenAsymptoticDistribution.TrendType - Enum in dev.nm.stat.cointegration
the available types of trends
JohansenTest - Class in dev.nm.stat.cointegration
The maximum number of cointegrating relations among a multivariate time series is the rank of the Π matrix.
JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest
Construct an instance of JohansenTest.
JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest
Construct an instance of JohansenTest.
JordanExchange - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
Jordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A.
JordanExchange() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
 

K

k - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
the shape parameter
k - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
k(Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
Evaluates the standard kinetic energy, k = p^2 / 2m.
K - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of distinct population eigenvalues bigger than 0; the length of t
K() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
Gets the H property.
K(ARMAModel) - Static method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
 
Kagi<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility
KAGI construction of a random process.
Kagi(double[]) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi
 
Kagi(UnivariateTimeSeries<T, ? extends UnivariateTimeSeries.Entry<T>>) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi
 
Kagi.Trend - Enum in tech.nmfin.meanreversion.hvolatility
 
KagiModel - Class in tech.nmfin.meanreversion.hvolatility
Maintains the states of a KAGI model.
KagiModel(double) - Constructor for class tech.nmfin.meanreversion.hvolatility.KagiModel
 
KendallRankCorrelation - Class in dev.nm.stat.descriptive.correlation
The Kendall rank correlation coefficient, commonly referred to as Kendall's tau (τ) coefficient, is a statistic used to measure the association between two measured quantities.
KendallRankCorrelation(double[], double[]) - Constructor for class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
Construct a Kendall rank calculator initialized with two samples.
Kernel - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
The kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0.
Kernel(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Construct the kernel of a matrix.
Kernel(Matrix, Kernel.Method, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Construct the kernel of a matrix.
Kernel.Method - Enum in dev.nm.algebra.linear.matrix.doubles.linearsystem
These are the available methods to compute kernel basis.
keySet() - Method in class dev.nm.combinatorics.Counter
Get the set of numbers the counter has seen.
KnightSatchellTran1995 - Class in tech.nmfin.trend.kst1995
Implements the Knight-Satchell-Tran model of financial asset returns.
KnightSatchellTran1995(double, double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Constructs an instance of the Knight-Satchell-Tran model of returns.
KnightSatchellTran1995(double, double, double, double, double, double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Constructs an instance of the Knight-Satchell-Tran model of returns.
KnightSatchellTran1995(KnightSatchellTran1995) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Copy constructor.
KnightSatchellTran1995MLE - Class in tech.nmfin.trend.kst1995
Fits a KST model from returns.
KnightSatchellTran1995MLE() - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995MLE
 
Knuth1969 - Class in dev.nm.stat.random.rng.univariate.poisson
This is a random number generator that generates random deviates according to the Poisson distribution.
Knuth1969(double) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
Constructs a random number generator to sample from the Poisson distribution.
Knuth1969(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
Constructs a random number generator to sample from the Poisson distribution.
KolmogorovDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.
KolmogorovDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Construct a Kolmogorov distribution for a sample size n.
KolmogorovDistribution(int, int, boolean) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Construct a Kolmogorov distribution for a sample size n.
KolmogorovOneSidedDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
Compute the probability that F(x) is dominated by the upper confidence contour, for all x: Pn(ε) = Pr{F(x) < min{Fn(x) + ε, 1}}
KolmogorovOneSidedDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Construct a one-sided Kolmogorov distribution.
KolmogorovOneSidedDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Construct a one-sided Kolmogorov distribution.
KolmogorovSmirnov - Class in dev.nm.stat.test.distribution.kolmogorov
The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test).
KolmogorovSmirnov.Side - Enum in dev.nm.stat.test.distribution.kolmogorov
the available types of the Kolmogorov-Smirnov statistic
KolmogorovSmirnov.Type - Enum in dev.nm.stat.test.distribution.kolmogorov
the available types of the Kolmogorov-Smirnov tests
KolmogorovSmirnov1Sample - Class in dev.nm.stat.test.distribution.kolmogorov
The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution.
KolmogorovSmirnov1Sample(double[], ProbabilityDistribution, KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
Construct a one-sample Kolmogorov-Smirnov test.
KolmogorovSmirnov2Samples - Class in dev.nm.stat.test.distribution.kolmogorov
The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples.
KolmogorovSmirnov2Samples(double[], double[], KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
Construct a two-sample Kolmogorov-Smirnov test.
KolmogorovTwoSamplesDistribution - Class in dev.nm.stat.test.distribution.kolmogorov
Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.
KolmogorovTwoSamplesDistribution(double[], double[], KolmogorovTwoSamplesDistribution.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Construct a two-sample Kolmogorov distribution.
KolmogorovTwoSamplesDistribution(int, int, double[], KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Construct a two-sample Kolmogorov distribution.
KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, double[]) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Construct a two-sample Kolmogorov distribution.
KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Construct a two-sample Kolmogorov distribution, assuming that there is no tie in the samples.
KolmogorovTwoSamplesDistribution.Side - Enum in dev.nm.stat.test.distribution.kolmogorov
the available types of Kolmogorov-Smirnov two-sample test
KroneckerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
Given an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, is an (mp)-by-(nq) matrix with entries defined by cst = aij bkl where
KroneckerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.KroneckerProduct
Construct the Kronecker product of two matrices.
KruskalWallis - Class in dev.nm.stat.test.rank
The Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups.
KruskalWallis(double[]...) - Constructor for class dev.nm.stat.test.rank.KruskalWallis
Construct a Kruskal-Wallis test for the equality of medians of groups.
KunduGupta2007 - Class in dev.nm.stat.random.rng.univariate.gamma
Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1.
KunduGupta2007(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
Constructs a random number generator to sample from the gamma distribution.
KunduGupta2007(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
Constructs a random number generator to sample from the gamma distribution.
kurtosis() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Gets the excess kurtosis of this distribution.
kurtosis() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
kurtosis() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the excess kurtosis of this distribution.
kurtosis() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.TDistribution
Gets the excess kurtosis of this distribution.
kurtosis() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
kurtosis() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
kurtosis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated.
kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated.
Kurtosis - Class in dev.nm.stat.descriptive.moment
Kurtosis measures the "peakedness" of the probability distribution of a real-valued random variable.
Kurtosis() - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
Construct an empty Kurtosis calculator.
Kurtosis(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
Construct a Kurtosis calculator, initialized with a sample.
Kurtosis(Kurtosis) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis
Copy constructor.

L

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
The sub-diagonal entries of the unit lower triangular matrix L.
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Get the lower triangular matrix L, such that P * A = L * U.
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
 
L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Cholesky
Get the lower triangular matrix L.
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
 
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
Get L as in the LDL decomposition.
L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
 
L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
Get the lower triangular matrix L as in the LU decomposition.
L() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Gets the lower triangular matrix L in the LDL decomposition.
Label(SimplexTable.LabelType, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
Construct a label for a row or column in the simplex table.
lag(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.
lag(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
Constructs an instance of SimpleTimeSeries by lagging the time series.
lag(int, int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.
lag(int, int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
Constructs an instance of SimpleTimeSeries by lagging the time series.
LaguerrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
Laguerre polynomials are defined by the recurrence relation below.
LaguerrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials
 
LaguerreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
 
LaguerreRule(int, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
Create a Laguerre rule of the given order.
Lai2010NPEBModel - Class in tech.nmfin.portfoliooptimization.lai2010
The Non-Parametric Empirical Bayes (NPEB) model described in the reference computes the optimal weights for asset allocation.
Lai2010NPEBModel(MVOptimizer, int) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel
Constructs an instance of the model using MomentsEstimatorLedoitWolf as the default estimator of covariance matrix, and assuming IID for each individual asset for re-sampling.
Lai2010NPEBModel(MVOptimizer, int, ReturnsMoments.Estimator, ReturnsResamplerFactory, CetaMaximizer) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel
Constructs an instance of the model.
Lai2010NPEBModel.OptimalWeights - Class in tech.nmfin.portfoliooptimization.lai2010
 
Lai2010OptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization
 
Lai2010OptimizationAlgorithm(double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
 
Lai2010OptimizationAlgorithm(MVOptimizer, ReturnsMoments.Estimator, ReturnsResamplerFactory, int, double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm
 
lambda - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
The norm of the generator with the sign chosen to be the opposite of the first coordinate of the generator.
lambda() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Gets sample eigenvalues.
lambda() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
Gets the penalization parameter for the unconstrained form of LASSO.
lambda() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
 
Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda
Stores the Beta distribution parameters.
Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
Stores the Gamma distribution parameters.
Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
Construct a Log-Normal distribution.
Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
Construct a Normal distribution.
Lambda(int, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
Stores the Binomial distribution parameters.
lambda_Jacobian - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
lambda Jacobian
lambda1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
lambda1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the transition intensity from bull market to bear market.
lambda2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
lambda2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the transition intensity from bear market to bull market.
lambdaCol - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
 
Lanczos - Class in dev.nm.analysis.function.special.gamma
The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.
Lanczos() - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos
Construct a Lanczos approximation instance using default parameters.
Lanczos(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos
Construct a Lanczos approximation instance.
LANCZOS - dev.nm.analysis.function.special.gamma.LogGamma.Method
Lanczos approximation.
LANCZOS_QUICK - dev.nm.analysis.function.special.gamma.LogGamma.Method
Quick Lanczos approximation, where all computations are done in double precision.
LARSFitting - Class in dev.nm.stat.regression.linear.lasso.lars
This class computes the entire LARS sequence of coefficients and fits, starting from zero to the OLS fit.
LARSFitting(LARSProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting
Estimates the entire LARS sequence of coefficients with the default epsilon and maximum number of steps.
LARSFitting(LARSProblem, double, int) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting
Estimates the entire LARS sequence of coefficients and fits, starting from zero to the OLS fit.
LARSFitting(LARSProblem, int) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting
Estimates the entire LARS sequence of coefficients with the default epsilon.
LARSFitting.Estimators - Class in dev.nm.stat.regression.linear.lasso.lars
 
LARSProblem - Class in dev.nm.stat.regression.linear.lasso.lars
Least Angle Regression (LARS) is a regression algorithm for high-dimensional data.
LARSProblem(Vector, Matrix) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Constructs a LASSO variation of the Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.
LARSProblem(Vector, Matrix, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.
LARSProblem(Vector, Matrix, boolean, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model.
LARSProblem(Vector, Matrix, boolean, boolean, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Constructs a Least Angel Regression (LARS) problem.
LARSProblem(LARSProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Copy constructor.
last() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
LAST - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
lastEdge(V) - Method in class dev.nm.graph.algorithm.shortestpath.Dijkstra
 
lastEdge(V) - Method in interface dev.nm.graph.algorithm.shortestpath.ShortestPath
Gets the last edge of a vertex on its shortest distance from the source.
lcm(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
Calculates the least common multiple of integer a and integer b.
LD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
 
LDDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Represents a L D LT decomposition of a shifted symmetric tridiagonal matrix T.
LDDecomposition(Vector, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
 
LDFactorizationFromRoot - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Decomposes (T - σ I) into L D LT where T is a symmetric tridiagonal matrix, σ is a shift for this factorization, L is a unit lower triangular matrix, and D is a diagonal matrix.
LDFactorizationFromRoot(Vector, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot
Creates a decomposition for a symmetric tridiagonal matrix T.
LDLt - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt.
LDLt(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
Run the LDL decomposition on a real and symmetric (hence square) matrix.
LDLt(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
Run the LDL decomposition on a real and symmetric (hence square) matrix.
LeapFrogging - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
The leap-frogging algorithm is a method for simulating Molecular Dynamics, which is time-reversible.
LeapFrogging(RealVectorFunction, Vector, Vector, Vector, double) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Constructs a new instance with the given parameters.
LeapFrogging.DynamicsState - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Contains the entire state (both the position and the momentum) at a given point in time.
LeastPth<T> - Class in dev.nm.solver.multivariate.minmax
The least p-th minmax algorithm minimizes the maximal error/loss (function): \[ \min_x \max_{\omega \in S} e(x, \omega) \] \(e(x, \omega)\) is the error or loss function.
LeastPth(double, int) - Constructor for class dev.nm.solver.multivariate.minmax.LeastPth
Construct a minmax minimizer using the Least p-th method.
LeastSquares - Class in dev.nm.analysis.curvefit
This method obtains a least squares estimate of a polynomial to fit the input data, by a weighted sum of orthogonal polynomials up to a specified order.
LeastSquares(int) - Constructor for class dev.nm.analysis.curvefit.LeastSquares
Construct a new instance of this algorithm, which uses uniform weighting for the observations.
LeastSquares(int, LeastSquares.Weighting) - Constructor for class dev.nm.analysis.curvefit.LeastSquares
Construct a new instance of the algorithm 4.5.1 from Schilling & Harris, which will use a weighted sum of orthogonal polynomials up to order n (the number of points).
LeastSquares.Weighting - Interface in dev.nm.analysis.curvefit
This interface defines a weighting for observations.
Lebesgue - Class in dev.nm.analysis.integration.univariate
Lebesgue integration is the general theory of integration of a function with respect to a general measure.
Lebesgue(double[], double[]) - Constructor for class dev.nm.analysis.integration.univariate.Lebesgue
Construct a Lebesgue integral.
LEcuyer - Class in dev.nm.stat.random.rng.univariate.uniform.linear
This is the uniform random number generator recommended by L'Ecuyer in 1996.
LEcuyer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
Construct a LEcuyer pseudo uniform random generator.
LEcuyer(long, long, long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
Construct a LEcuyer pseudo uniform random generator and then seed.
LECUYER - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
Lecuyer
LedoitWolf2004 - Class in dev.nm.stat.covariance
To estimate the covariance matrix, Ledoit and Wolf (2004) suggests using the matrix obtained from the sample covariance matrix through a transformation called shrinkage.
LedoitWolf2004() - Constructor for class dev.nm.stat.covariance.LedoitWolf2004
Creates the algorithm instance, using an unbiased sample covariance matrix by default.
LedoitWolf2004(boolean) - Constructor for class dev.nm.stat.covariance.LedoitWolf2004
Creates the algorithm instance, with the option to use an unbiased or biased sample covariance matrix.
LedoitWolf2004.Result - Class in dev.nm.stat.covariance
The estimator and some intermediate values computed by the algorithm.
LedoitWolf2016 - Class in dev.nm.stat.covariance.nlshrink
This is Ledoit's non-linear shrinkage method for computing covariance matrixes when the dimension is large compared to the number of observations.
LedoitWolf2016() - Constructor for class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
 
LedoitWolf2016(boolean) - Constructor for class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
 
LedoitWolf2016.Result - Class in dev.nm.stat.covariance.nlshrink
the estimator and some intermediate values computed by the algorithm
leftConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T
Get the one sided left confidence interval, [0, a]
leftConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
Compute the one sided left confidence interval, [0, a]
leftMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Left multiplies a matrix.
leftOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T
Get the left, one-sided p-value.
leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey
Get the left, one-sided p-value.
leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Get the left, one-sided p-value.
leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Get the left, one-sided p-value.
leftOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F
Get the left, one-sided p-value.
leftShift(double...) - Static method in class dev.nm.number.DoubleUtils
Perform a in-memory left-shift (by 1 cell} to an array.
leftShift(double[], int) - Static method in class dev.nm.number.DoubleUtils
Perform a in-memory right-shift (by k cells} to an array.
leftShift(T[]) - Static method in class dev.nm.misc.ArrayUtils
Get a left shifted array.
leftShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils
Get a left shifted (by 1 cell) copy of an array.
leftShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils
Get a left shifted (by k cells) copy of an array.
LegendrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
A Legendre polynomial is defined by the recurrence relation below.
LegendrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials
 
LegendreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
 
LegendreRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
Create a Legendre rule of the given order.
Lehmer - Class in dev.nm.stat.random.rng.univariate.uniform.linear
Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].
Lehmer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
Construct a Lehmer (pure) linear congruential generator.
Lehmer(long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
Construct a Lehmer (pure) linear congruential generator.
Lehmer(long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
Construct a skipping ahead Lehmer (pure) linear congruential generator.
LEHMER - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
Lehmer
length() - Method in class dev.nm.analysis.sequence.Fibonacci
 
length() - Method in interface dev.nm.analysis.sequence.Sequence
Get the number of computed terms in the sequence.
length() - Method in class dev.nm.geometry.LineSegment
Get the length of the line segment.
LESS - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
compute Dn-; check whether the cdf of a sample lies below the null hypothesis
LESS - dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
two-sample; one-sided
LessThanConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint
The domain of an optimization problem may be restricted by less-than or equal-to constraints.
Levene - Class in dev.nm.stat.test.variance
The Levene test tests for the equality of variance of groups.
Levene(double...) - Constructor for class dev.nm.stat.test.variance.Levene
Perform the Levene test to test for equal variances across the groups.
Levene(Levene.Type, double[]...) - Constructor for class dev.nm.stat.test.variance.Levene
Perform the Levene test to test for equal variances across the groups.
Levene.Type - Enum in dev.nm.stat.test.variance
the available implementations when computing the absolute deviations
leverage() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the leverage.
License - Class in dev.nm.misc.license
This is the license management system for the library.
LICENSE_FILE_PROPERTY - Static variable in class dev.nm.misc.license.License
The system property term for setting license file.
LicenseError - Error in dev.nm.misc.license
General error regarding the license, e.g., errors when loading license.
LicenseError(String) - Constructor for error dev.nm.misc.license.LicenseError
 
LIGHT_SPEED_C - Static variable in class dev.nm.misc.PhysicalConstants
The speed of light in a vacuum \(c\), \(c_0\) in meters per second (m s-1).
Lilliefors - Class in dev.nm.stat.test.distribution.normality
Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
Lilliefors(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.Lilliefors
Perform the Lilliefors test to test for the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.
LILSparseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
The list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value.
LILSparseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
Construct a sparse matrix in LIL format.
LILSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
Construct a sparse matrix in LIL format.
LILSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
Construct a sparse matrix in LIL format by a list of non-zero entries.
LILSparseMatrix(LILSparseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
Copy constructor.
LINEAR_INTERPOLATION_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the linear interpolation of the empirical cdf
LinearCongruentialGenerator - Interface in dev.nm.stat.random.rng.univariate.uniform.linear
A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.
LinearConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is a collection of linear constraints for a real-valued optimization problem.
LinearConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
Construct a collection of linear constraints.
linearEqualities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
linearEqualityConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
 
linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
Deprecated.
 
linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
 
linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
 
linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
 
LinearEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is a collection of linear equality constraints.
LinearEqualityConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints
Construct a collection of linear equality constraints.
LinearFit - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Find the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values.
LinearFit() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
Create an instance with the assumption of c = 2.
LinearFit(double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit
This fitting assumes c is a constant.
linearGreaterThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
 
linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
Deprecated.
 
linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
 
linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
 
linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
 
LinearGreaterThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is a collection of linear greater-than-or-equal-to constraints.
LinearGreaterThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
Construct a collection of linear greater-than or equal-to constraints.
linearInequalities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
linearInterpolate(double, double, double, double, double) - Static method in class dev.nm.analysis.function.FunctionOps
Linear interpolation between two points.
LinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.univariate
(Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent data-points.
LinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.LinearInterpolation
 
LinearInterpolator - Class in dev.nm.analysis.curvefit.interpolation
Define a univariate function by linearly interpolating between adjacent points.
LinearInterpolator(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
Construct a univariate function by linearly interpolating between adjacent points.
LinearKalmanFilter - Class in dev.nm.stat.dlm.univariate
The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.
LinearKalmanFilter(DLM) - Constructor for class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Construct a Kalman filter from a univariate controlled dynamic linear model.
linearLessThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint
 
linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
Deprecated.
 
linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
 
linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
 
linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
 
LinearLessThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is a collection of linear less-than-or-equal-to constraints.
LinearLessThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
Construct a collection of linear less-than or equal-to constraints.
LinearModel - Interface in dev.nm.stat.regression.linear
A linear model provides fitting and the residual analysis (goodness of fit).
LinearRepresentation - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
LinearRepresentation(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
Construct the linear representation of an ARMA model up to the default number of lags LinearRepresentation.DEFAULT_NUMBER_OF_LAGS.
LinearRepresentation(ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation
Construct the linear representation of an ARMA model.
LinearRoot - Class in dev.nm.analysis.function.polynomial.root
This is a solver for finding the roots of a linear equation.
LinearRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.LinearRoot
 
LinearSystemSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
Solve a system of linear equations in the form: Ax = b, We assume that, after row reduction, A has no more rows than columns.
LinearSystemSolver(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
Construct a solver for a linear system of equations.
LinearSystemSolver.NoSolution - Exception in dev.nm.algebra.linear.matrix.doubles.linearsystem
This is the runtime exception thrown when it fails to solve a system of linear equations.
LinearSystemSolver.Solution - Interface in dev.nm.algebra.linear.matrix.doubles.linearsystem
This is the solution to a linear system of equations.
linesearch - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
linesearch(Vector, Vector) - Method in interface dev.nm.solver.multivariate.unconstrained.c2.linesearch.LineSearch.Solution
Get the increment α so that f(x + α * d) is (approximately) minimized.
LineSearch - Interface in dev.nm.solver.multivariate.unconstrained.c2.linesearch
A line search is often used in another minimization algorithm to improve the current solution in one iteration step.
LineSearch.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2.linesearch
This is the solution to a line search minimization.
LineSegment - Class in dev.nm.geometry
Represent a line segment.
LineSegment(Point, Point) - Constructor for class dev.nm.geometry.LineSegment
Create a line segment with two given endpoints.
link() - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily
Get the link function of this distribution.
LinkCloglog - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the complementary log-log link function: g(x) = log(-log(1 - x))
LinkCloglog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog
 
LinkFunction - Interface in dev.nm.stat.regression.linear.glm.distribution.link
This interface represents a link function g(x) in Generalized Linear Model (GLM).
LinkIdentity - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the identity link function: g(x) = x
LinkIdentity() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity
 
LinkInverse - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the inverse link function: g(x) = 1/x
LinkInverse() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse
 
LinkInverseSquared - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the inverse-squared link function: g(x) = 1/x2
LinkInverseSquared() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared
 
LinkLog - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the log link function: g(x) = log(x)
LinkLog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog
 
LinkLogit - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the logit link function: \[ g(x) = \log(\frac{\mu}{1-\mu}) \]
LinkLogit() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit
 
LinkProbit - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1).
LinkProbit() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit
 
LinkSqrt - Class in dev.nm.stat.regression.linear.glm.distribution.link
This class represents the square-root link function: g(x) = sqrt(x)
LinkSqrt() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt
 
linshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Gets linear shrinkage tau in ascending order.
LjungBox - Class in dev.nm.stat.test.timeseries.portmanteau
The Ljung-Box test (named for Greta M.
LjungBox(double[], int, int) - Constructor for class dev.nm.stat.test.timeseries.portmanteau.LjungBox
Perform the Ljung-Box test to check auto-correlation in a time series.
LLD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
 
LMBeta - Class in dev.nm.stat.regression.linear
Beta coefficients are the outcomes of fitting a linear regression model.
LMBeta(Vector) - Constructor for class dev.nm.stat.regression.linear.LMBeta
Constructs an instance of Beta.
LMDiagnostics - Class in dev.nm.stat.regression.linear.residualanalysis
This class collects some diagnostics measures for the goodness of fit based on the residulas for a linear regression model.
LMDiagnostics(LMResiduals) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics
Constructs an instance of the Diagnostics from the results of residual analysis.
LMInformationCriteria - Class in dev.nm.stat.regression.linear.residualanalysis
The information criteria measure the goodness of fit of an estimated statistical model.
LMInformationCriteria(LMResiduals) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria
Computes the information criteria from residual analysis.
LMProblem - Class in dev.nm.stat.regression.linear
This is a linear regression or a linear model (LM) problem.
LMProblem(Vector, Matrix) - Constructor for class dev.nm.stat.regression.linear.LMProblem
Constructs a linear regression problem, assuming a constant term (the intercept) equal weights assigned to all observations
LMProblem(Vector, Matrix, boolean) - Constructor for class dev.nm.stat.regression.linear.LMProblem
Constructs a linear regression problem, assuming equal weights to all observations.
LMProblem(Vector, Matrix, boolean, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem
Constructs a linear regression problem.
LMProblem(Vector, Matrix, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem
Constructs a linear regression problem, assuming a constant term (the intercept).
LMProblem(LMProblem) - Constructor for class dev.nm.stat.regression.linear.LMProblem
Copy constructor.
LMResiduals - Class in dev.nm.stat.regression.linear.residualanalysis
This is the residual analysis of the results of a linear regression model.
LMResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Performs residual analysis for a linear regression problem.
loading(int) - Method in interface dev.nm.stat.factor.pca.PCA
Gets the loading vector of the i-th principal component.
loading(int) - Method in class dev.nm.stat.factor.pca.PCAbyEigen
 
loadings() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Gets the rotated loading matrix.
loadings() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the matrix of variable loadings.
loadings() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
 
loadings() - Method in class dev.nm.stat.factor.pca.PCAbySVD
 
LocalDateInterval - Class in dev.nm.misc.datastructure.time
Represents an interval between two LocalDate.
localDatePanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
 
LocalDateTimeInterval - Class in dev.nm.misc.datastructure.time
Represents an interval between two LocalDateTime.
localDateTimePanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
 
LocalDateTimeUtils - Class in dev.nm.misc.datastructure.time
Utility functions to manipulate LocalDateTime.
LocalDateTimeUtils() - Constructor for class dev.nm.misc.datastructure.time.LocalDateTimeUtils
 
LocalSearchCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
 
LocalSearchCellFactory<P extends OptimProblem,​T extends IterativeMinimizer<OptimProblem>> - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T>, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory
Construct an instance of a LocalSearchCellFactory.
LocalSearchCellFactory.LocalSearchCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
A LocalSearchCell implements the two genetic operations.
LocalSearchCellFactory.MinimizerFactory<U extends IterativeMinimizer<OptimProblem>> - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
This factory constructs a new Minimizer for each mutation operation.
log(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the logs of values.
log(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the log of a vector, element-by-element.
log(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Natural logarithm of a complex number.
log(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute log(x).
log(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute log(x) up to a scale.
LOG - tech.nmfin.returns.ReturnsCalculators
The return is defined as the natural logarithm of the ratio v2/v1.
logAcceptanceRatio(RealScalarFunction, Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisUtils
Computes the log of the acceptance ratio.
LogBeta - Class in dev.nm.analysis.function.special.beta
This class represents the log of Beta function log(B(x, y)).
LogBeta() - Constructor for class dev.nm.analysis.function.special.beta.LogBeta
 
logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
logDensity(double) - Method in interface dev.nm.stat.evt.evd.univariate.UnivariateEVD
Get the logarithm of the probability density function at \(x\), that is, \(\log(f(x))\).
logGamma(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
Compute log-gamma for a positive value x.
logGamma(BigDecimal) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
Compute log-gamma for a positive value x to arbitrary precision.
LogGamma - Class in dev.nm.analysis.function.special.gamma
The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.
LogGamma() - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma
Construct an instance of log-Gamma.
LogGamma(LogGamma.Method, Lanczos) - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma
Construct an instance of log-Gamma.
LogGamma.Method - Enum in dev.nm.analysis.function.special.gamma
the available methods to compute \(\log (\Gamma(z))\)
logGammaQuick(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos
Compute log-gamma for a positive value x.
LogisticBeta - Class in dev.nm.stat.regression.linear.logistic
Beta coefficient estimator, β^, of a logistic regression model.
LogisticBeta(Vector, LogisticResiduals) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticBeta
Construct an instance of Beta.
LogisticProblem - Class in dev.nm.stat.regression.linear.logistic
A logistic regression problem is a variation of the OLS regression problem.
LogisticProblem(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticProblem
Constructs a logistic regression problem from a linear regression problem.
LogisticRegression - Class in dev.nm.stat.regression.linear.logistic
A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.
LogisticRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression
Constructs a Logistic instance.
LogisticRegression(LogisticProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression
Constructs a Logistic instance.
LogisticResiduals - Class in dev.nm.stat.regression.linear.logistic
Residual analysis of the results of a logistic regression.
logLikelihood - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel
the log-likelihood
logLikelihood() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Compute the log-likelihood at the fitted value.
logLikelihood() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Gets the log-likelihood value.
logLikelihood() - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
Gets the likelihood of the given observations.
logLikelihood() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
 
logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.IWLS
 
logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
logLikelihood(LogisticProblem) - Static method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
Constructs the log-likelihood function for a logistic regression problem.
logMu - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
the log-mean μ ∈ R
LogNormalDistribution - Class in dev.nm.stat.distribution.univariate
A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.
LogNormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.LogNormalDistribution
Construct a log-normal distribution.
LogNormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Log-Normal distribution to model the observations.
LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
Constructs a log-normal distribution for each state in the HMM model.
LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
Constructs a log-normal distribution for each state in the HMM model.
LogNormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
the log-normal distribution parameters
LogNormalRNG - Class in dev.nm.stat.random.rng.univariate
This random number generator samples from the log-normal distribution.
LogNormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG
Construct a random number generator to sample from the log-normal distribution.
LogNormalRNG(NormalRNG) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG
Construct a random number generator to sample from the log-normal distribution.
logProbability(int[], double[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
Gets the probability of observing the observations and having gone thru the state sequence.
logProbability(int[], int[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
Gets the probability of observing the observations and having gone thru the state sequence.
logProbability(HmmInnovation[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel
Gets the probability of observing the observations and having gone thru the state sequence.
logSigma - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda
the log-standard deviation; shape
longValue() - Method in class dev.nm.number.complex.Complex
Deprecated.
Invalid operation.
longValue() - Method in class dev.nm.number.Real
 
longValue() - Method in class dev.nm.number.ScientificNotation
 
LoopBody - Interface in dev.nm.misc.parallel
The implementation of this interface contains the code inside a for-loop construct.
lower - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
lower() - Method in class dev.nm.interval.RealInterval
Get the lower bound of this interval.
lower() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
 
LOWER - dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
a lower bi-diagonal matrix, where there are only non-zero entries on the main and sub diagonal
LOWER - dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
diverge near x = a
lowerBound() - Method in class dev.nm.analysis.function.polynomial.CauchyPolynomial
Cauchy's lower bound on polynomial zeros is the unique positive root of the Cauchy polynomial.
lowerBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
Gets the lower bounds.
lowerBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Split the box constraints and get the greater-than-the-lower-bounds part.
LowerBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is a lower bound constraints such that for all xi's, xi ≥ b
LowerBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LowerBoundConstraints
Construct a lower bound constraints for all variables in a function.
lowerBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Gets the lower bounds.
LowerTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
A lower triangular matrix has 0 entries where column index > row index.
LowerTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Constructs a lower triangular matrix from a 2D double[][] array.
LowerTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Constructs a lower triangular matrix of dimension dim * dim.
LowerTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Constructs a lower triangular matrix from a matrix.
LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
Copy constructor.
LPBoundedMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
This is the solution to a bounded linear programming problem.
LPBoundedMinimizer(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
Constructs the solution for a bounded linear programming problem.
LPCanonicalProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
This is a linear programming problem in the 1st canonical form (following the convention in the reference): min c'x s.t.
LPCanonicalProblem1(Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
Construct a linear programming problem in the canonical form.
LPCanonicalProblem1(Vector, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
Construct a linear programming problem in the canonical form.
LPCanonicalProblem1(LPCanonicalProblem2) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1
Convert a linear programming problem from the 2nd canonical form to the 1st canonical form.
LPCanonicalProblem2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
This is a linear programming problem in the 2nd canonical form (following the convention in the wiki): min c'x s.t.
LPCanonicalProblem2(Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
Construct a linear programming problem in the canonical form.
LPCanonicalProblem2(Vector, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
Construct a linear programming problem in the canonical form.
LPCanonicalProblem2(LPCanonicalProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2
Convert a linear programming problem from the 1st canonical form to the 2nd canonical form.
LPCanonicalSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
This is an LP solver that solves a canonical LP problem in the following form.
LPCanonicalSolver() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPCanonicalSolver
 
LPDimensionNotMatched - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.
LPDimensionNotMatched(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPDimensionNotMatched
Construct an instance of LPDimensionNotMatched.
LPEmptyCostVector - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when there is no objective function in a linear programming problem.
LPEmptyCostVector() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPEmptyCostVector
 
LPException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when there is any problem when solving a linear programming problem.
LPException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPException
Construct an instance of LPException.
LPInfeasible - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when the LP problem is infeasible, i.e., no solution.
LPInfeasible() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPInfeasible
 
LPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.
LPNoConstraint - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when there is no linear constraint found for the LP problem.
LPNoConstraint() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPNoConstraint
 
LPProblem - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.
LPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
This is an implementation of a linear programming problem, LPProblem.
LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
Construct a general linear programming problem with only greater-than-or-equal-to and equality constraints.
LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
Construct a general linear programming problem.
LPRevisedSimplexSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
 
LPRevisedSimplexSolver(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
 
LPRevisedSimplexSolver.Problem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
 
LPRuntimeException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when there is any problem when constructing a linear programming problem.
LPRuntimeException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
Construct an instance of LPRuntimeException.
LPRuntimeException(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException
Construct an instance of LPRuntimeException.
LPSimplexMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
A simplex LP minimizer can be read off from the solution simplex table.
LPSimplexSolution - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
The solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer.
LPSimplexSolver<P extends LPProblem> - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.
LPSolution<T extends LPMinimizer> - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
A solution to an LP problem contains all information about solving an LP problem such as whether the problem has a solution (bounded), how many minimizers it has, and the minimum.
LPSolver<P extends LPProblem,​S extends LPSolution<?>> - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp
An LP solver solves a Linear Programming (LP) problem.
LPStandardProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
This is a linear programming problem in the standard form: min c'x s.t.
LPStandardProblem(Vector, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPStandardProblem
Construct a linear programming problem in the standard form.
LPTwoPhaseSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
This implementation solves a linear programming problem, LPProblem, using a two-step approach.
LPTwoPhaseSolver() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
Construct an LP solver to solve LP problems.
LPTwoPhaseSolver(LPSimplexSolver<LPCanonicalProblem1>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
Construct an LP solver to solve LP problems.
LPUnbounded - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception
This is the exception thrown when the LP problem is unbounded.
LPUnbounded(int) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
Construct an instance of LPUnbounded.
LPUnboundedMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
This is the solution to an unbounded linear programming problem.
LPUnboundedMinimizer(SimplexTable, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
Construct the solution for an unbounded linear programming problem.
LPUnboundedMinimizerScheme2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution
This is the solution to an unbounded linear programming problem found in scheme 2.
LPUnboundedMinimizerScheme2(SimplexTable, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
Construct the solution for an unbounded linear programming problem as a result of applying scheme 2.
LSProblem - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
This is the problem of solving a system of linear equations.
LSProblem(Matrix, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Constructs a system of linear equations Ax = b.
Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol
Gets the transpose of the lower triangular matrix, L'.
Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt
Get the transpose of L as in the LDL decomposition.
Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Gets the transpose of the lower triangular matrix L in the LDL decomposition.
LU - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
LU(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
Run the LU decomposition on a square matrix.
LU(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
Run the LU decomposition on a square matrix.
LUDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
LU decomposition decomposes an n x n matrix A so that P * A = L * U.
LUSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
Use LU decomposition to solve Ax = b where A is square and det(A) != 0.
LUSolver() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
 

M

m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Gets the number of interior x-axis grid points in the solution.
m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the number of interior time-axis grid points in the solution.
m() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets the dimension of the system, i.e., m = the dimension of y.
m() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets the dimension of the system, i.e., m = the dimension of y.
m() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the number of covariates (number of columns of X), excluding the intercept.
M() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Gets the number of interior time-axis grid points in the solution.
m0() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM
Get the the mean of x0.
m0() - Method in class dev.nm.stat.dlm.univariate.DLM
Get the the mean of x0.
MA() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
Get the MA coefficients.
MA(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the i-th MA coefficient; MA(0) = 1.
MA(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the i-th MA coefficient; MA(0) = 1.
MACH_EPS - Static variable in class dev.nm.misc.Constants
the machine epsilon
MACH_SCALE - Static variable in class dev.nm.misc.Constants
the scale for the machine epsilon
MADecomposition - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess
This class decomposes a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method with symmetric window.
MADecomposition(double[], double[], int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Decompose a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method.
MADecomposition(double[], int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Decompose a periodic time series into the seasonal and stationary random components using no MA filter.
MADecomposition(double[], int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition
Decompose a time series into the trend, seasonal and stationary random components using the default filter.
MAGNETIC_FLUX_QUANTUM_PHI0 - Static variable in class dev.nm.misc.PhysicalConstants
The magnetic flux quantum \(\Phi_0\) in webers (Wb).
MAGNETIC_MU0 - Static variable in class dev.nm.misc.PhysicalConstants
The magnetic constant \(\mu_0\) in henries per meter (H m-1) or newtons per ampere squared (N A-2).
main(String[]) - Static method in class dev.nm.misc.license.FloatingLicenseServer
 
MAModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
This class represents a univariate MA model.
MAModel(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Construct a univariate MA model with unit variance and zero-mean.
MAModel(double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Construct a univariate MA model with zero-mean.
MAModel(double, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Construct a univariate MA model with unit variance.
MAModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Construct a univariate MA model.
MAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel
Copy constructor.
mapClusterIndices(List<List<Integer>>) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
marginalInverseTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Inverse of marginal transform.
marginalTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
Transform to exponential margins under the GEV model.
MarketImpact1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Constructs the constraint coefficient arrays of a market impact term in the compact form.
MarketImpact1(Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
Constructs a market impact term.
MarkowitzByCLM - Class in tech.nmfin.portfoliooptimization.markowitz
Solves for the optimal weights in the Markowitz formulation by critical line method.
MarkowitzByCLM(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ 0.
MarkowitzByCLM(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ w_lower, w ≤ w_upper.
MarkowitzByCLM(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM
Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.
MarkowitzByQP - Class in tech.nmfin.portfoliooptimization.markowitz
Modern portfolio theory (MPT) is a theory of investment which attempts to maximize portfolio expected return for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets.
MarkowitzByQP(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Constructs a Markowitz portfolio from expected future returns and future covariance, assuming no short selling constraint and zero benchmark rate.
MarkowitzByQP(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Constructs a Markowitz portfolio from expected future returns and future covariance, with lower and upper limits on asset weights, assuming zero benchmark rate.
MarkowitzByQP(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.
MarkowitzByQP(Vector, Matrix, QPConstraint) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Constructs a Markowitz portfolio from expected future returns and future covariance, assuming zero benchmark rate for Sharpe ratio calculation.
MarkowitzByQP(Vector, Matrix, QPConstraint, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Constructs a Markowitz portfolio from expected future returns and future covariance.
MarkowitzCriticalLine - Interface in tech.nmfin.portfoliooptimization.clm
 
MARMAModel - Class in dev.nm.stat.evt.timeseries
Simulation of max autoregressive moving average processes, i.e., MARMA(p, q) processes.
MARMAModel(double[], double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel
Create an instance with the AR and MA coefficients, using FrechetDistribution as the GEV distribution.
MARMAModel(double[], double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel
Create an instance with the AR and MA coefficients, and a GEV distribution for generating innovations.
MARMAModel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel
Create an instance with a given GEV distribution for generating innovations.
MARMASim - Class in dev.nm.stat.evt.timeseries
Generate random numbers based on a given MARMA model.
MARMASim(MARMAModel) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim
Create an instance with the given MARMAModel.
MARMASim(MARMAModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim
Create an instance with the given MARMAModel, but override the innovation generation by the the given generator.
MARMASim(MARMAModel, RandomNumberGenerator, double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim
Create an instance with the given MARMAModel and initial values, but override the innovation generation by the the given generator.
MARModel - Class in dev.nm.stat.evt.timeseries
This is equivalent to MARMA(p, 0).
MARModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARModel
Create an instance with the AR coefficients, using FrechetDistribution as the GEV distribution.
MARModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARModel
Create an instance with the AR coefficients, and a GEV distribution for generating innovations.
MarsagliaBray1964 - Class in dev.nm.stat.random.rng.univariate.normal
The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.
MarsagliaBray1964() - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
Construct a random number generator to sample from the standard Normal distribution.
MarsagliaBray1964(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
Construct a random number generator to sample from the standard Normal distribution.
MarsagliaTsang2000 - Class in dev.nm.stat.random.rng.univariate.gamma
Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.
MarsagliaTsang2000() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
Construct a random number generator to sample from the standard gamma distribution.
MarsagliaTsang2000(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
Construct a random number generator to sample from the gamma distribution.
MarsagliaTsang2000(double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
Construct a random number generator to sample from the gamma distribution.
Mass(X, double) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
Creates an instance with an outcome and its associated probability.
MAT - Class in dev.nm.algebra.linear.matrix.doubles.operation
MAT is the inverse operator of SVEC.
MAT(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MAT
Constructs the MAT of a vector.
MathTable - Class in dev.nm.misc.datastructure
A mathematical table consists of numbers showing the results of calculation with varying arguments.
MathTable(int) - Constructor for class dev.nm.misc.datastructure.MathTable
Constructs an empty table.
MathTable(String...) - Constructor for class dev.nm.misc.datastructure.MathTable
Constructs an empty table by headers.
MathTable.Row - Class in dev.nm.misc.datastructure
A row is indexed by a number and contains multiple values.
Matrix - Interface in dev.nm.algebra.linear.matrix.doubles
This interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition.
MatrixAccess - Interface in dev.nm.algebra.linear.matrix.doubles
This interface defines the methods for accessing entries in a matrix.
MatrixAccessException - Exception in dev.nm.algebra.linear.matrix
This is the runtime exception thrown when trying to access an invalid entry in a matrix, e.g., A[0, 0].
MatrixAccessException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixAccessException
Construct an instance of MatrixAccessException.
MatrixAccessException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixAccessException
Construct an instance of MatrixAccessException with a message.
MatrixCoordinate - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
The location of a matrix entry is specified by a 2D coordinates (i, j), where i and j are the row-index and column-index of the entry respectively.
MatrixCoordinate(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
Construct a matrix coordinate specifying an entry location.
MatrixFactory - Class in dev.nm.algebra.linear.matrix.doubles.operation
These are the utility functions to create a new matrix/vector from existing ones.
MatrixMathOperation - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
This interface defines some standard operations for generic matrices.
MatrixMeasure - Class in dev.nm.algebra.linear.matrix.doubles.operation
A measure, μ, of a matrix, A, is a map from the Matrix space to the Real line.
MatrixMeasure() - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
 
MatrixMismatchException - Exception in dev.nm.algebra.linear.matrix
This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.
MatrixMismatchException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException
Construct an instance of MatrixMismatchException.
MatrixMismatchException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException
Construct an instance of MatrixMismatchException with a message.
MatrixPropertyUtils - Class in dev.nm.algebra.linear.matrix.doubles
These are the boolean operators that take matrices or vectors and check if they satisfy a certain property.
MatrixRing - Interface in dev.nm.algebra.linear.matrix.doubles
A matrix ring is the set of all n × n matrices over an arbitrary Ring R.
MatrixRootByDiagonalization - Class in dev.nm.algebra.linear.matrix.doubles.operation
The square root of a matrix extends the notion of square root from numbers to matrices.
MatrixRootByDiagonalization(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MatrixRootByDiagonalization
Constructs the square root of a Matrix by diagonalization.
MatrixSingularityException - Exception in dev.nm.algebra.linear.matrix
This is the runtime exception thrown when an operation acts on a singular matrix, e.g., applying LU decomposition to a singular matrix.
MatrixSingularityException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixSingularityException
Construct an instance of MatrixSingularityException.
MatrixSingularityException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixSingularityException
Construct an instance of MatrixSingularityException with a message.
MatrixTable - Interface in dev.nm.algebra.linear.matrix.doubles
A matrix is represented by a rectangular table structure with accessors.
MatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.operation
These are the utility functions to apply to matrices.
MatthewsDavies - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.
MatthewsDavies(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies
Constructs a symmetric, positive definite matrix using the Matthews-Davies algorithm.
max(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the maximum of the values.
max(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the maximum of the values.
max(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the maximal entry in a matrix.
max(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Returns the later of the two given time instances, or the first instance if two instances are equal.
Max - Class in dev.nm.stat.descriptive.rank
The maximum of a sample is the biggest value in the sample.
Max() - Constructor for class dev.nm.stat.descriptive.rank.Max
Construct an empty Max calculator.
Max(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Max
Construct a Max calculator, initialized with a sample.
Max(Max) - Constructor for class dev.nm.stat.descriptive.rank.Max
Copy constructor.
MAX - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
max_abs_cor() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the estimated sequence of maximal absolute correlations.
MAX_D - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
MAX_ITERATION - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
MAX_ITERATIONS_EXCEEDED - dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
Thrown if the iterative algorithm fails to converge to a solution within a specified tolerance after the specified maximum number of iterations.
MAX_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
MAX_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
maxBinSize() - Method in class dev.nm.misc.algorithm.Bins
Gets the maximal size of the bins.
maxClusterSize() - Method in class dev.nm.graph.community.GirvanNewman
Get the size of the maximal cluster.
maxDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Get the biggest abscissae.
maxEdge() - Method in class dev.nm.graph.community.EdgeBetweeness
Gets the edge with the maximal edge-betweeness.
maxEdge() - Method in class dev.nm.graph.community.GirvanNewman
Gets the edge with the maximal edge-betweeness.
MaximaDistribution - Class in dev.nm.stat.evt.evd.univariate
The distribution of \(M\), where \(M=\max(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).
MaximaDistribution(ProbabilityDistribution, int) - Constructor for class dev.nm.stat.evt.evd.univariate.MaximaDistribution
Create an instance with the probability distribution of \(X\), and the number of iid samples to be drawn.
MaximizationSolution<T> - Interface in dev.nm.solver
This is the solution to a maximization problem.
maximizer() - Method in interface dev.nm.solver.MaximizationSolution
Get the maximizer (solution) to the maximization problem.
maximum() - Method in interface dev.nm.solver.MaximizationSolution
Get the (approximate) maximum found.
MaximumLikelihoodFitting - Interface in dev.nm.stat.evt.evd.univariate.fitting
This interface defines model fitting by maximum likelihood algorithm.
maxIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils
Get the index of the maximum of the values, skipping Double.NaN.
maxIndex(double...) - Static method in class dev.nm.number.DoubleUtils
Get the index of the maximum of the values, skipping Double.NaN.
maxIterations - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
the maximum number of iterations
maxIterations - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
 
maxIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
maxIterations - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
the maximum number of iterations
MaxIterationsExceededException(int) - Constructor for exception dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException
Construct a new MaxIterationsExceededException, indicating the number of iterations.
Maxmizer<P extends OptimProblem,​S extends MaximizationSolution<?>> - Interface in dev.nm.solver
This interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension.
maxOrder() - Method in class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences
Get the maximum order which is limited by the number of points given for the computation.
maxPQ() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the maximum of AR length or MA length.
maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the maximum of AR length or MA length.
maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the maximum of the ARCH length or GARCH length.
maxValue() - Method in class dev.nm.graph.community.EdgeBetweeness
Gets the maximum of edge-betweeness-es.
maxValue() - Method in class dev.nm.graph.community.GirvanNewman
Get the maximum of edge-betweeness.
maxZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
McCormickMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
Deprecated.
the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.
McCormickMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.McCormickMinimizer
Deprecated.
the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.
MCLNiedermayer - Class in tech.nmfin.portfoliooptimization.clm
Implements Markowitz's critical line algorithm.
MCLNiedermayer(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
Creates the critical line for given gain vector and covariance matrix, with non-negativity constraint.
MCLNiedermayer(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
Creates the critical line for given gain vector and covariance matrix, with given lower and upper bounds for weights.
MCUtils - Class in dev.nm.stat.markovchain
These are the utility functions to examine a Markov chain.
mean() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
Get the sample mean.
mean() - Method in class dev.nm.stat.descriptive.moment.Skewness
Get the sample mean.
mean() - Method in class dev.nm.stat.descriptive.moment.Variance
Get the sample mean.
mean() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
mean() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
mean() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
Gets the mean of this distribution.
mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Gets the mean of this distribution.
mean() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
mean() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the mean of this distribution.
mean() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.TDistribution
Gets the mean of this distribution.
mean() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
mean() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
mean() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
\[ \mu + \frac{\sigma}{1-\xi} \] for \(\xi < 1\).
mean() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
mean() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
mean() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
mean() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the sample means that were subtracted.
mean() - Method in class dev.nm.stat.factor.pca.PCAbySVD
 
mean() - Method in interface dev.nm.stat.random.Estimator
Gets the expectation of the estimator.
mean() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
Compute the mean of the integral.
mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
mean() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
mean() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
Computes the sample mean of the in-sample spread.
mean() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
 
mean() - Method in interface tech.nmfin.meanreversion.volarb.MeanPriceEstimator
 
Mean - Class in dev.nm.stat.descriptive.moment
The mean of a sample is the sum of all numbers in the sample, divided by the sample size.
Mean() - Constructor for class dev.nm.stat.descriptive.moment.Mean
Construct an empty Mean calculator.
Mean(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Mean
Construct a Mean calculator, initialized with a sample.
Mean(Mean) - Constructor for class dev.nm.stat.descriptive.moment.Mean
Copy constructor.
MEAN_REVERSION - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
 
mean1() - Method in class dev.nm.stat.test.mean.T
Get the mean of the first sample.
mean2() - Method in class dev.nm.stat.test.mean.T
Get the mean of the second sample.
MeanEstimator - Interface in dev.nm.stat.random
 
MeanEstimatorMaxLevelShift - Class in tech.nmfin.meanreversion.volarb
 
MeanEstimatorMaxLevelShift(int, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
 
MeanEstimatorMaxLevelShift(int, double, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
 
MeanPriceEstimator - Interface in tech.nmfin.meanreversion.volarb
Defines how to estimate the mean price.
MEANS - dev.nm.stat.test.variance.Levene.Type
compute the absolute deviations from the group means
median() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
Gets the median of this distribution.
median() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Deprecated.
Not supported yet.
median() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
median() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the median of this distribution.
median() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
median() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
\[ \mu + \frac{\sigma( 2^{\xi} -1)}{\xi} \]
median() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
median() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
median() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
median() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated.
median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated.
MEDIAN - dev.nm.stat.test.variance.Levene.Type
compute the absolute deviations from the group medians
MEET - dev.nm.interval.IntervalRelation
X meets Y.
MEET_INVERSE - dev.nm.interval.IntervalRelation
Y meets X.
mergeIntervals(RealInterval[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils
 
MERSENNE_TWISTER - dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
Mersenne Twister (recommended)
MersenneExponent - Enum in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
The value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister random number generator with a period of 2p.
MersenneTwister - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister
Mersenne Twister is one of the best pseudo random number generators available.
MersenneTwister() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
Constructs a random number generator to sample uniformly from [0, 1).
MersenneTwister(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
Constructs a random number generator to sample uniformly from [0, 1).
MersenneTwister(MersenneTwisterParam) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
Constructs a new instance, which uses parameters and the state contained in the given MersenneTwisterParam instance.
MersenneTwister(MersenneTwisterParam, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
 
MersenneTwisterParam - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister
Immutable parameters for creating a MersenneTwister RNG.
MersenneTwisterParam() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
MersenneTwisterParam(int, int, int, int, int, int, int, int, int, int, int, int, int, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam
 
MersenneTwisterParamSearcher - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation
Searches for Mersenne-Twister parameters.
MersenneTwisterParamSearcher(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
Constructs a new instance which uses the given RNG to do the parameter search.
MersenneTwisterParamSearcher(RandomLongGenerator, MersenneExponent) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
Constructs a new instance which uses the given RNG to do the parameter search, with the given period parameter.
Metropolis - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
This basic Metropolis implementation assumes using symmetric proposal function.
Metropolis(RealScalarFunction, Vector, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, with the given variance and zero covariance.
Metropolis(RealScalarFunction, Vector, Matrix, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, multiplied by the given scale matrix.
Metropolis(RealScalarFunction, RealVectorFunction, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
Constructs a new instance with the given parameters.
MetropolisAcceptanceProbabilityFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
Uses the classic Metropolis rule, f_{t+1}/f_t.
MetropolisAcceptanceProbabilityFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction
 
MetropolisHastings - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
A generalization of the Metropolis algorithm, which allows asymmetric proposal functions.
MetropolisHastings(RealScalarFunction, ProposalFunction, MetropolisHastings.ProposalDensityFunction, Vector, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
Constructs a new instance with the given parameters.
MetropolisHastings.ProposalDensityFunction - Interface in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Defines the density of a proposal function, i.e.
MetropolisUtils - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Utility functions for Metropolis algorithms.
Midpoint - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes
The midpoint rule computes an approximation to a definite integral, made by finding the area of a collection of rectangles whose heights are determined by the values of the function.
Midpoint(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Midpoint
Construct an integrator that implements the Midpoint rule.
MIDWAY_THROUGH_STEPS_OF_EMPIRICAL_CDF - dev.nm.stat.descriptive.rank.Quantile.QuantileType
a piecewise linear function where the knots are the values midway through the steps of the empirical cdf
MilsteinSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete
Milstein scheme is a first-order approximation to a continuous-time SDE.
MilsteinSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE
Discretize a continuous-time SDE using the Milstein scheme.
min() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
 
min(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the minimum of the values.
min(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the minimal entry in a matrix.
min(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Returns the earlier of the two given time instances, or the first instance if two instances are equal.
Min - Class in dev.nm.stat.descriptive.rank
The minimum of a sample is the smallest value in the sample.
Min() - Constructor for class dev.nm.stat.descriptive.rank.Min
Construct an empty Min calculator.
Min(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Min
Construct a Min calculator, initialized with a sample.
Min(Min) - Constructor for class dev.nm.stat.descriptive.rank.Min
Copy constructor.
MIN - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
MIN_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
MIN_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
 
minDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Get the smallest abscissae.
MinimaDistribution - Class in dev.nm.stat.evt.evd.univariate
The distribution of \(M\), where \(M=\min(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).
MinimaDistribution(ProbabilityDistribution, int) - Constructor for class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
MinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular).
MinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
Construct a MINRES solver.
MinimalResidualSolver(PreconditionerFactory, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
Construct a MINRES solver.
MinimizationSolution<T> - Interface in dev.nm.solver
This is the solution to a minimization problem.
minimizer() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
 
minimizer() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
minimizer() - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
 
minimizer() - Method in interface dev.nm.solver.MinimizationSolution
Get the minimizer (solution) to the minimization problem.
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
This is the same as the u vector, such that the direction of arbitrarily negative can be computed by adjusting λ.
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
 
minimizer() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution
Get a minimizing vector.
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
 
minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
Minimizer<P extends OptimProblem,​S extends MinimizationSolution<?>> - Interface in dev.nm.solver
This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.
minimizers() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
Get all optimal minimizers.
minimum() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
 
minimum() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
minimum() - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
 
minimum() - Method in interface dev.nm.solver.MinimizationSolution
Get the (approximate) minimum found.
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
Get the (approximate) minimum found.
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
Get the (approximate) minimum found.
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
Deprecated.
minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
 
minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
MinimumWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
This constraint puts lower bounds on weights.
MinimumWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.MinimumWeights
 
minIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils
Get the index of the minimum of the values, skipping Double.NaN.
minIndex(double...) - Static method in class dev.nm.number.DoubleUtils
Get the index of the minimum of the values, skipping Double.NaN.
MINITAB_SPSS - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the definition in Minitab and SPSS
MinMaxMinimizer<T> - Interface in dev.nm.solver.multivariate.minmax
A minmax minimizer minimizes a minmax problem.
MinMaxProblem<T> - Interface in dev.nm.solver.multivariate.minmax
A minmax problem is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain.
minorMatrix(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Gets the minor matrix of a given matrix, by removing a specified row and a specified column.
minus(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
minus(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Subtract a constant from all entries in this vector.
minus(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
 
minus(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
Subtract one double array from another, entry-by-entry.
minus(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
minus(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
minus(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
this - that
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Computes the difference between two diagonal matrices.
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
minus(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
A1 - A2
minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
minus(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Subtract the elements in this by that, element-by-element.
minus(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
minus(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
minus(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
minus(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
minus(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
minus(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
minus(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
\(this - that\)
minus(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Subtracts a constant from a vector, element-by-element.
minus(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
A vector subtracts another vector, element-by-element.
minus(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
minus(Complex) - Method in class dev.nm.number.complex.Complex
 
minus(Real) - Method in class dev.nm.number.Real
 
minus(G) - Method in interface dev.nm.algebra.structure.AbelianGroup
- : G × G → G
minZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
MixedRule - Class in dev.nm.analysis.integration.univariate.riemann.substitution
The mixed rule is good for functions that fall off rapidly at infinity, e.g., \(e^{x^2}\) or \(e^x\) The integral region is \((0, +\infty)\).
MixedRule(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.MixedRule
Construct a MixedRule substitution rule.
MixtureDistribution - Interface in dev.nm.stat.hmm.mixture.distribution
This is the conditional distribution of the observations in each state (possibly differently parameterized) of a mixture hidden Markov model.
MixtureHMM - Class in dev.nm.stat.hmm.mixture
This is the mixture hidden Markov model (HMM).
MixtureHMM(Vector, Matrix, MixtureDistribution) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM
Constructs a mixture hidden Markov model.
MixtureHMM(MixtureHMM) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM
Copy constructor.
MixtureHMMEM - Class in dev.nm.stat.hmm.mixture
The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm.
MixtureHMMEM(double[], MixtureHMM, double, int) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMMEM
Constructs a mixture HMM model by training an initial model using the Baum-Welch algorithm.
MixtureHMMEM.TrainedModel - Class in dev.nm.stat.hmm.mixture
the result of the EM algorithm
ML() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
Gets the maximum log-likelihood.
MMAModel - Class in dev.nm.stat.evt.timeseries
This is equivalent to MARMA(0, q).
MMAModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel
Create an instance with the MA coefficients, using FrechetDistribution as the GEV distribution.
MMAModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel
Create an instance with the MA coefficients, and a GEV distribution for generating innovations.
mod(long, long) - Static method in class dev.nm.analysis.function.FunctionOps
Compute the positive modulus of a number.
mode() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
mode() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
mode() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
Gets the mode of this distribution.
mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
mode() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
model - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel
the newly trained model as a result of the EM algorithm
ModelNotFound(String) - Constructor for exception dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection.ModelNotFound
Construct a ModelNotFound exception with an error message.
ModelParam(double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
ModelParam(double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
ModelResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
 
ModelResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
 
ModelResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
 
modpow(long, long, long) - Static method in class dev.nm.analysis.function.FunctionOps
be mod m
modulus() - Method in class dev.nm.number.complex.Complex
Get the modulus.
modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
 
modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
 
modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
 
modulus() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator
Get the modulus of this linear congruential generator.
modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
 
MOLAR_GAS_R - Static variable in class dev.nm.misc.PhysicalConstants
The molar gas constant \(R\) in joule per kelvin mole (J mol-1 K-1).
moment(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
Deprecated.
Not supported yet.
moment(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Deprecated.
Not supported yet.
moment(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
moment(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
The moment generating function is the expected value of etX.
moment(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
moment(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
moment(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
moment(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated.
moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated.
moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
 
moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
 
moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
 
moment(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution
The moment generating function is the expected value of etX.
moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
 
moment(Vector) - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
 
moment1() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
 
moment2() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
 
moments() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
 
Moments - Class in dev.nm.stat.descriptive.moment
Compute the central moment of a data set incrementally.
Moments(int) - Constructor for class dev.nm.stat.descriptive.moment.Moments
Construct an empty moment calculator, computing all moments up to and including the order-th moment.
Moments(int, double...) - Constructor for class dev.nm.stat.descriptive.moment.Moments
Construct a moment calculator, computing all moments up to and including the order-th moment.
Moments(Moments) - Constructor for class dev.nm.stat.descriptive.moment.Moments
Copy constructor.
MomentsEstimatorLedoitWolf - Class in tech.nmfin.returns.moments
Estimates the first two moments of returns using Ledoit-Wolf 2004.
MomentsEstimatorLedoitWolf() - Constructor for class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
 
MOMENTUM - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
 
Monoid<G> - Interface in dev.nm.algebra.structure
A monoid is a group with a binary operation (×), satisfying the group axioms: closure associativity existence of multiplicative identity
moveColumn2End(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Swaps a column of a permutation matrix with the last column.
moveRow2End(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Swaps a row of the permutation matrix with the last row.
MovingAverage - Class in dev.nm.dsp.univariate.operation.system.doubles
This applies a linear filter to a univariate time series using the moving average estimation.
MovingAverage(double[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
Construct a moving average filter using a symmetric window.
MovingAverage(double[], MovingAverage.Side) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
Construct a moving average filter.
MovingAverage.Side - Enum in dev.nm.dsp.univariate.operation.system.doubles
the available types of moving average filtering
MovingAverageByExtension - Class in dev.nm.dsp.univariate.operation.system.doubles
This implements a moving average filter with these properties: 1) both past and future observations are used in smoothing; 2) the head is prepended with the first element in the inputs (x_t = x_1 for t < 1); 3) the tail is appended with the last element in the inputs (x_t = x_n for t > n).
MovingAverageByExtension(double[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension
Construct a moving average filter with prepending and appending.
MR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
MR3 - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
For a symmetric matrix.
MR3 - dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
Multiple Relatively Robust Representation (MRRR), for higher speed.
MR3(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Creates an instance for computing eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T.
MR3(Vector, Vector, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T.
MR3(Vector, Vector, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T.
MRG - Class in dev.nm.stat.random.rng.univariate.uniform.linear
A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:
MRG(long, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
Construct a Multiple Recursive Generator.
MRModel - Interface in tech.nmfin.meanreversion.volarb
A Mean Reversion Model computes the target position given the current price.
MRModelRanged - Class in tech.nmfin.meanreversion.volarb
 
MRModelRanged(double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
 
MRModelRanged(double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged
 
mu - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
the mean
mu - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
mu() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
Gets μ as in E(Y) = μ = g-1(Xβ)
mu() - Method in class dev.nm.stat.regression.linear.glm.IWLS
 
mu() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
mu() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Get the drift: \(\mu(t,X_t,Z_t,...)\).
mu() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
 
mu() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
Get the overall mean.
mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the intercept (constant) vector.
mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the intercept vector.
mu() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the intercept (constant) term.
mu() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets the long term mean.
mu() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
 
mu() - Method in class tech.nmfin.returns.moments.ReturnsMoments
Gets the mean vector.
mu(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the convection coefficient at a given time t and a position x.
mu1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
mu1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the drift in the bull market.
mu2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
mu2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the drift in the bear market.
MultiCubicSpline - Class in dev.nm.analysis.curvefit.interpolation.multivariate
Implementation of natural cubic spline interpolation for an arbitrary number of dimensions.
MultiCubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiCubicSpline
Create an instance with CubicSpline as the implementation of the univariate cubic spline interpolation algorithm.
MultiDimensionalArray<T> - Class in dev.nm.misc.datastructure
A generic multi-dimensional array, with an arbitrary number of dimensions.
MultiDimensionalArray(int...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray
Creates an instance with the specified size along each dimension.
MultiDimensionalArray(MultiDimensionalCollection<T>) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray
A copy constructor that constructs a shallow copy of the given collection of instances.
MultiDimensionalCollection<T> - Interface in dev.nm.misc.datastructure
A generic collection with an arbitrary number of dimensions.
MultiDimensionalGrid - Class in dev.nm.misc.datastructure
An arbitrary dimensional grid.
MultiDimensionalGrid(double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
Constructs a multi-dimensional grid of points.
MultiDimensionalGrid(MultiDimensionalGrid.Discretization...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
Constructs a multi-dimensional grid of points.
MultiDimensionalGrid(Double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid
Constructs a multi-dimensional grid of points.
MultiDimensionalGrid.Discretization - Class in dev.nm.misc.datastructure
Specifies the discretization of an interval.
MultiLinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.multivariate
Implementation of linear interpolation for an arbitrary number of dimensions.
MultiLinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiLinearInterpolation
Create an instance with LinearInterpolation as the implementation of the univariate linear interpolation algorithm.
MultinomialBetaFunction - Class in dev.nm.analysis.function.special.beta
A multinomial Beta function is defined as: \[ \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left(\sum_{i=1}^K \alpha_i\right)},\qquad\boldsymbol{\alpha}=(\alpha_1,\cdots,\alpha_K) \]
MultinomialBetaFunction(int) - Constructor for class dev.nm.analysis.function.special.beta.MultinomialBetaFunction
Constructs an instance of a multinomial Beta function.
MultinomialDistribution - Class in dev.nm.stat.distribution.multivariate
 
MultinomialDistribution(int, double...) - Constructor for class dev.nm.stat.distribution.multivariate.MultinomialDistribution
Constructs an instance of a Multinomial distribution.
MultinomialRVG - Class in dev.nm.stat.random.rng.multivariate
A multinomial distribution puts N objects into K bins according to the bins' probabilities.
MultinomialRVG(int, double[]) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG
Constructs a multinomial random vector generator.
MultinomialRVG(int, double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG
Constructs a multinomial random vector generator.
MultipleExecutionException - Exception in dev.nm.misc.parallel
This exception is thrown when any of the parallel tasks throws an exception during execution.
MultipleExecutionException(List<?>, List<ExecutionException>) - Constructor for exception dev.nm.misc.parallel.MultipleExecutionException
Construct an exception with the (partial) results and all exceptions encountered during execution.
MultiplicativeModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess
The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.
MultiplicativeModel(double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
Construct a univariate time series by multiplying the components.
MultiplicativeModel(double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel
Construct a univariate time series by multiplying the components.
MultiplierPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
A multiplier penalty function allows different weights to be assigned to the constraints.
MultiplierPenalty(Constraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
Construct a multiplier penalty function from a collection of constraints.
MultiplierPenalty(Constraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
Construct a multiplier penalty function from a collection of constraints.
MultiplierPenalty(Constraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
Construct a multiplier penalty function from a collection of constraints.
multiply(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
 
multiply(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
Multiply one double array to another, entry-by-entry.
multiply(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
multiply(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
multiply(double[], double[], double[], int, int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplication
Multiplies two matrices, C = A %*% B.
multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock
 
multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
multiply(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
this * that
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
this * that
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Computes the product of two diagonal matrices.
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Left multiplication by G, namely, G * A.
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Left multiplication by P.
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
multiply(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
A1 * A2
multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
multiply(MatrixAccess, Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
A * v
multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
multiply(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
multiply(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
multiply(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
multiply(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
multiply(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
multiply(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
Right multiply this matrix, A, by a vector.
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Left multiplication by P.
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
multiply(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Multiply this by that, entry-by-entry.
multiply(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Multiplies two vectors, element-by-element.
multiply(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
multiply(Complex) - Method in class dev.nm.number.complex.Complex
Compute the product of this complex number and that complex number.
multiply(Real) - Method in class dev.nm.number.Real
 
multiply(G) - Method in interface dev.nm.algebra.structure.Monoid
× : G × G → G
multiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Left multiplication by G, namely, G * A.
multiplyInPlace(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Right multiplies this matrix, A, by a vector.
MultipointHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
A multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamics simulation, we pick a proposal from a window of the last M configurations.
MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
Constructs a new instance with the given parameters.
MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
Constructs a new instance with equal weights to the M configurations.
MultivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.multivariate
Implementation of MultivariateGrid, backed by the given MultiDimensionalCollection instance.
MultivariateArrayGrid(MultiDimensionalCollection<Double>, double[]...) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.
MultivariateAutoCorrelationFunction - Class in dev.nm.stat.timeseries.linear.multivariate
This is the auto-correlation function of a multi-dimensional time series {Xt}.
MultivariateAutoCorrelationFunction() - Constructor for class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
 
MultivariateAutoCovarianceFunction - Class in dev.nm.stat.timeseries.linear.multivariate
This is the auto-covariance function of a multi-dimensional time series {Xt}, \[ K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)') \] For a stationary process, the auto-covariance depends only on the lag, |i - j|.
MultivariateAutoCovarianceFunction() - Constructor for class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction
 
MultivariateBrownianRRG - Class in dev.nm.stat.stochasticprocess.multivariate.random
This is the Random Walk construction of a multivariate Brownian motion.
MultivariateBrownianRRG(int, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at evenly spaced time points [0, 1, ...].
MultivariateBrownianRRG(int, TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
MultivariateBrownianRRG(int, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.
MultivariateBrownianSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
A multivariate Brownian motion is a stochastic process with the following properties.
MultivariateBrownianSDE(int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
Construct a standard multi-dimensional Brownian motion.
MultivariateBrownianSDE(Vector, Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
Construct a multi-dimensional Brownian motion.
MultivariateDiscreteSDE - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
This interface represents the discrete approximation of a multivariate SDE.
MultivariateDLM - Class in dev.nm.stat.dlm.multivariate
This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.
MultivariateDLM(Vector, Matrix, MultivariateObservationEquation, MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM
Construct a (multivariate) controlled dynamic linear model.
MultivariateDLM(MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM
Copy constructor.
MultivariateDLMSeries - Class in dev.nm.stat.dlm.multivariate
This is a simulator for a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
Simulate a multivariate controlled dynamic linear model process.
MultivariateDLMSeries.Entry - Class in dev.nm.stat.dlm.multivariate
This is the TimeSeries.Entry for a multivariate DLM time series.
MultivariateDLMSim - Class in dev.nm.stat.dlm.multivariate
This is a simulator for a multivariate controlled dynamic linear model process.
MultivariateDLMSim(MultivariateDLM, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSim
Simulates a multivariate controlled dynamic linear model process.
MultivariateDLMSim.Innovation - Class in dev.nm.stat.dlm.multivariate
a simulated innovation
MultivariateEulerSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
The Euler scheme is the first order approximation of an SDE.
MultivariateEulerSDE(MultivariateSDE) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
Discretize a multivariate, continuous-time SDE using the Euler scheme.
MultivariateExponentialFamily - Class in dev.nm.stat.distribution.multivariate.exponentialfamily
The exponential family is an important class of probability distributions sharing this particular form.
MultivariateExponentialFamily(RealScalarFunction, RealVectorFunction, RealVectorFunction, RealScalarFunction) - Constructor for class dev.nm.stat.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily
Construct a factory to construct probability distribution in the exponential family of this form.
MultivariateFiniteDifference - Class in dev.nm.analysis.differentiation.multivariate
A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.
MultivariateFiniteDifference(RealScalarFunction, int[]) - Constructor for class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference
Construct the partial derivative of a multi-variable function.
MultivariateForecastOneStep - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess
The innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time series with known auto-covariance and these properties (not limited to ARMA processes): {xt} can be non-stationary. E(xt) = 0 for all t.
MultivariateForecastOneStep(MultivariateIntTimeTimeSeries, MultivariateAutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
MultivariateFt - Class in dev.nm.stat.stochasticprocess.multivariate.sde
This represents the concept 'Filtration', the information available at time t.
MultivariateFt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Construct an empty filtration (no information).
MultivariateFt(MultivariateFt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Copy constructor.
MultivariateFtWt - Class in dev.nm.stat.stochasticprocess.multivariate.sde
This is a filtration implementation that includes the path-dependent information, Wt.
MultivariateFtWt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
Construct an empty filtration (no information).
MultivariateFtWt(MultivariateFtWt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
Copy constructor.
MultivariateGenericTimeTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.multivariate
This is a multivariate time series indexed by some notion of time.
MultivariateGenericTimeTimeSeries(T[], double[][]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGenericTimeTimeSeries(T[], Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGenericTimeTimeSeries(T[], Vector[]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Construct a multivariate time series from timestamps and vectors.
MultivariateGrid - Interface in dev.nm.analysis.curvefit.interpolation.multivariate
A multivariate rectilinear (not necessarily uniform) grid of double values.
MultivariateGridInterpolation - Interface in dev.nm.analysis.curvefit.interpolation.multivariate
Interpolation on a rectilinear multi-dimensional grid.
MultivariateInnovationAlgorithm - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess
This class implements the part of the innovation algorithm that computes the prediction error covariances, V and prediction coefficients Θ.
MultivariateInnovationAlgorithm(int, MultivariateAutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
Run the Innovation Algorithm to compute the prediction parameters, V and Θ.
MultivariateIntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
This is a multivariate time series indexed by integers.
MultivariateIntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
This is the TimeSeries.Entry for an integer -indexed multivariate time series.
MultivariateLinearKalmanFilter - Class in dev.nm.stat.dlm.multivariate
The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.
MultivariateLinearKalmanFilter(MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Construct a Kalman filter from a multivariate controlled dynamic linear model.
MultivariateMinimizer<P extends OptimProblem,​S extends MinimizationSolution<Vector>> - Interface in dev.nm.solver.multivariate.unconstrained
This is a minimizer that minimizes a multivariate function or a Vector function.
MultivariateNormalDistribution - Class in dev.nm.stat.distribution.multivariate
The multivariate Normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) Normal distribution to higher dimensions.
MultivariateNormalDistribution(int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
Constructs an instance of the standard Normal distribution.
MultivariateNormalDistribution(Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
Constructs an instance with the given mean and covariance matrix.
MultivariateObservationEquation - Class in dev.nm.stat.dlm.multivariate
This is the observation equation in a controlled dynamic linear model.
MultivariateObservationEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Constructs a time-invariant an observation equation.
MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Constructs a time-invariant an observation equation.
MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Constructs an observation equation.
MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Constructs an observation equation.
MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Copy constructor.
MultivariateObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Constructs a multivariate observation equation from a univariate observation equation.
MultivariateProbabilityDistribution - Interface in dev.nm.stat.distribution.multivariate
A multivariate or joint probability distribution for X, Y, ... is a probability distribution that gives the probability that each of X, Y, ... falls in any particular range or discrete set of values specified for that variable.
MultivariateRandomProcess - Class in dev.nm.stat.stochasticprocess.multivariate.random
This interface represents a multivariate random process a.k.a.
MultivariateRandomProcess(int, TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Construct a multivariate random process.
MultivariateRandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.multivariate.random
This interface defines a generator to construct random realizations from a multivariate stochastic process.
MultivariateRandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.multivariate.random
This class generates random realizations from a multivariate random/stochastic process.
MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate random/stochastic process.
MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate discrete SDE.
MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate SDE.
MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
Construct a random realization generator from a multivariate SDE.
MultivariateRandomWalk - Class in dev.nm.stat.stochasticprocess.multivariate.random
This is the Random Walk construction of a multivariate stochastic process per SDE specification.
MultivariateRandomWalk(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
Construct a multivariate stochastic process from an SDE.
MultivariateRealization - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime
A multivariate realization is a multivariate time series indexed by real numbers, e.g., real time.
MultivariateRealization.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime
This is the TimeSeries.Entry for a real number -indexed multivariate time series.
MultivariateRegularGrid - Class in dev.nm.analysis.curvefit.interpolation.multivariate
A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.g.
MultivariateRegularGrid(MultiDimensionalCollection<Double>, MultivariateRegularGrid.EquallySpacedVariable...) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.
MultivariateRegularGrid.EquallySpacedVariable - Class in dev.nm.analysis.curvefit.interpolation.multivariate
Specify the positioning and spacing along one dimension.
MultivariateResampler - Interface in dev.nm.stat.random.sampler.resampler.multivariate
This is the interface of a multivariate re-sampler method.
MultivariateSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde
This class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form: \[ dX_t = \mu(t,X_t,Z_t,...)*dt + \sigma(t, X_t, Z_t, ...)*dB_t \]
MultivariateSDE(DriftVector, DiffusionMatrix, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Construct a multi-dimensional diffusion type stochastic differential equation.
MultivariateSimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
This simple multivariate time series has its vectored values indexed by integers.
MultivariateSimpleTimeSeries(double[]...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(Vector...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries.
MultivariateSimpleTimeSeries(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
Construct an instance of MultivariateSimpleTimeSeries from a univariate time series.
MultivariateStateEquation - Class in dev.nm.stat.dlm.multivariate
This is the state equation in a controlled dynamic linear model.
MultivariateStateEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a time-invariant state equation without control variables.
MultivariateStateEquation(Matrix, Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a time-invariant state equation.
MultivariateStateEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a state equation without control variables.
MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a state equation.
MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a state equation.
MultivariateStateEquation(MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Copy constructor.
MultivariateStateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Constructs a multivariate state equation from a univariate state equation.
MultivariateTDistribution - Class in dev.nm.stat.distribution.multivariate
The multivariate T distribution or multivariate Student distribution, is a generalization of the one-dimensional (univariate) Student's t-distribution to higher dimensions.
MultivariateTDistribution(int, int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
Constructs an instance of the standard t distribution, mean 0, variance 1.
MultivariateTDistribution(int, Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
Constructs an instance with the given mean and scale matrix.
MultivariateTimeSeries<T extends Comparable<? super T>,​E extends MultivariateTimeSeries.Entry<T>> - Interface in dev.nm.stat.timeseries.datastructure.multivariate
A multivariate time series is a sequence of vectors indexed by some notion of time.
MultivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.multivariate
This is the TimeSeries.Entry for a multivariate time series.
mutate() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome
Construct a Chromosome by mutation.
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell
 
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell
 
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell
 
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell
 
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell
Mutate by a local search in the neighborhood.
mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
Mutate by random disturbs in a neighborhood.
Mutex - Class in dev.nm.misc.parallel
Provides mutual exclusive execution of a Runnable.
Mutex() - Constructor for class dev.nm.misc.parallel.Mutex
 
MVOptimizer - Interface in tech.nmfin.portfoliooptimization.lai2010.optimizer
Solves for the optimal weight using Mean-Variance optimization.
MVOptimizerLongOnly - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
A long-only MV optimizer.
MVOptimizerLongOnly() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly
 
MVOptimizerMinWeights - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
Solves for weights with lower bounds.
MVOptimizerMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
Constructs the solver with a constraint on the minimum weights (w ≥ w0).
MVOptimizerNoConstraint - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
Solves for optimal weights by closed-form expressions of w(η) when there is no limit on short selling.
MVOptimizerNoConstraint() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint
 
MVOptimizerShrankMean - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer
Shrinks the mean towards average before passing the inputs to another MVOptimizer.
MVOptimizerShrankMean(MVOptimizer, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean
 
MWC8222 - Class in dev.nm.stat.random.rng.univariate.uniform
Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.
MWC8222() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.MWC8222
Construct a random number generator to sample uniformly from [0, 1].
MyCutter(ILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter
Construct a Gomory mixed cutter.
MyCutter(PureILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter
Construct a Gomory pure cutter.
MySteepestDescent(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
 

N

n - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
n - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
sample size
n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Gets the number of interior y-axis grid points in the solution.
n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the number of interior x-axis grid points in the solution.
n() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets the dimension of the square matrices C and As.
n() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets the dimension of the system, i.e., the dimension of x, the number of variables.
n() - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the number of rows of the multivariate time series used in regression.
n(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets the number of columns of Ai.
n(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets the number of columns of \({A^q}_i\).
N - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
the number of observations
N - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
N() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
Get the number of data points.
N() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Gets the number of interior space-axis grid points in the solution.
N() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
 
N() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
 
N() - Method in class dev.nm.stat.descriptive.covariance.Covariance
 
N() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
 
N() - Method in class dev.nm.stat.descriptive.moment.Mean
 
N() - Method in class dev.nm.stat.descriptive.moment.Moments
 
N() - Method in class dev.nm.stat.descriptive.moment.Skewness
 
N() - Method in class dev.nm.stat.descriptive.moment.Variance
 
N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
 
N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
N() - Method in class dev.nm.stat.descriptive.rank.Max
 
N() - Method in class dev.nm.stat.descriptive.rank.Min
 
N() - Method in class dev.nm.stat.descriptive.rank.Quantile
 
N() - Method in interface dev.nm.stat.descriptive.Statistic
Get the size of the sample.
N() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
 
N() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
Gets the number of H inversions.
n_l() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
n_q() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Gets the total number of \({A^q}_i\) matrices.
n_u() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
 
NA - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
 
NaiveRule - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
This pivoting rule chooses the column with the most negative reduced cost.
NaiveRule() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
 
name - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
the variable name
NaN - Static variable in class dev.nm.number.complex.Complex
a number representing the complex Not-a-Number (NaN)
natural() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
Constructs an instance with end conditions which fits natural splines, meaning that the second derivative at both ends are zero.
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Get the dimension of the Brownian motion (or the number of driving 1D Brownian motions).
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
 
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated.
 
nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix
Get the number of independent Brownian motions.
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE
 
nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE
Get the number of independent driving Brownian motions.
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
 
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the number of independent driving Brownian motions.
nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Get the number of driving Brownian motions.
nChildren() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Get the number of children before populating the next generation.
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
nCols() - Method in class dev.nm.misc.datastructure.FlexibleTable
 
nCols() - Method in interface dev.nm.misc.datastructure.Table
Gets the number of columns.
nColumns() - Method in class dev.nm.misc.datastructure.MathTable
Gets the number of columns in the table.
NEAREST_EVEN_ORDER_STATISTICS - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the nearest even order statistic as in SAS
NEGATIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex
a number representing -∞ + -∞i
NegCetaFunction(Ceta) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
 
neighbor(V) - Method in interface dev.nm.graph.Edge
Gets the neighboring vertex connected to vertex.
neighbor(V) - Method in class dev.nm.graph.type.SimpleArc
 
neighbor(V) - Method in class dev.nm.graph.type.SimpleEdge
Get the unique neighboring vertex connected to vertex.
neighbors(V) - Method in interface dev.nm.graph.HyperEdge
Gets the set of neighboring vertices connected to vertex.
neighbors(V) - Method in class dev.nm.graph.type.SimpleArc
 
neighbors(V) - Method in class dev.nm.graph.type.SimpleEdge
 
NelderMeadMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2
The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems.
NelderMeadMinimizer(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
Construct a Nelder-Mead multivariate minimizer.
NelderMeadMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
Construct a Nelder-Mead multivariate minimizer.
NelderMeadMinimizer.Solution - Class in dev.nm.solver.multivariate.unconstrained.c2
This is the solution to an optimization problem by the Nelder-Mead method.
nEqualities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
Get the number of equality constraints.
NEUTRON_MASS_M - Static variable in class dev.nm.misc.PhysicalConstants
The neutron mass in kilograms (kg).
NevilleTable - Class in dev.nm.analysis.curvefit.interpolation
Neville's algorithm is a polynomial interpolation algorithm.
NevilleTable() - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
Construct an empty Neville table.
NevilleTable(int, OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
Construct a Neville table of size n, initialized with data {(x, y)}.
NevilleTable(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable
Construct a Neville table of size n, initialized with data {(x, y)}.
newActiveList() - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer.ActiveListFactory
Construct a new instance of ActiveList for an Integer Linear Programming problem.
newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.NewCellFactory
Construct a new instance of DEOptimCellFactory for a minimization problem.
newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.NewCellFactoryCtor
Construct a new instance of SimpleCellFactory for a minimization problem.
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
 
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
 
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
 
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
 
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
 
newDistributions() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
Get the distributions (possibly differently parameterized) for all states.
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
 
newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
 
newInstance() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.ConstrainedMinimizerFactory
Constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.
newInstance() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory
Construct a new instance of Minimizer for a mutation operation.
newInstance(double[], int, int, DoubleArrayOperation) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
newInstance(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.PreconditionerFactory
Construct a new instance of Preconditioner for a coefficient matrix.
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
Construct a new distribution from a set of parameters, one set per state.
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
 
newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution
 
newRandomNumberGenerators() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution
Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
 
newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
 
newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
 
newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
 
newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
 
newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
 
newResample() - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
 
newResample() - Method in interface dev.nm.stat.random.sampler.resampler.multivariate.MultivariateResampler
Gets a resample from the original sample.
newResample() - Method in interface dev.nm.stat.random.sampler.resampler.ObjectResampler
Gets a resample from the original sample.
newResample() - Method in interface dev.nm.stat.random.sampler.resampler.Resampler
Gets a resample from the original sample.
newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
 
newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory
 
newResampler(Matrix) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ReturnsResamplerFactory
Constructs a new instance of a re-sampling mechanism.
newSOCPGeneralConstraints(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
Creates a new SOCPGeneralConstraints so we can add SOCPGeneralConstraint to it.
newSOCPLinearEqualities(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
newSOCPLinearInequalities(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
NewtonCotes - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes
The Newton-Cotes formulae, also called the Newton-Cotes quadrature rules or simply Newton-Cotes rules, are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points.
NewtonCotes(int, NewtonCotes.Type, double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
Construct an instance of the Newton-Cotes quadrature.
NewtonCotes.Type - Enum in dev.nm.analysis.integration.univariate.riemann.newtoncotes
There are two types of the Newton-Cotes method: OPEN and CLOSED.
NewtonPolynomial - Class in dev.nm.analysis.curvefit.interpolation.univariate
Newton polynomial is the interpolation polynomial for a given set of data points in the Newton form.
NewtonPolynomial() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.NewtonPolynomial
 
NewtonRaphsonImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl
 
NewtonRaphsonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series.
NewtonRaphsonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
Construct a multivariate minimizer using the Newton-Raphson method.
NewtonRaphsonMinimizer.NewtonRaphsonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
 
NewtonRoot - Class in dev.nm.analysis.root.univariate
The Newton-Raphson method is as follows: one starts with an initial guess which is reasonably close to the true root, then the function is approximated by its tangent line (which can be computed using the tools of calculus), and one computes the x-intercept of this tangent line (which is easily done with elementary algebra).
NewtonRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.NewtonRoot
Constructs an instance of Newton's root finding algorithm.
NewtonSystemRoot - Class in dev.nm.analysis.root.multivariate
This class solves the root for a non-linear system of equations.
NewtonSystemRoot(double, int) - Constructor for class dev.nm.analysis.root.multivariate.NewtonSystemRoot
Constructs an instance of Newton's root finding algorithm for a system of non-linear equations.
newVariation(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.VariationFactory
Construct a new instance of SQPASVariation for an SQP problem.
newVariation(RealScalarFunction, EqualityConstraints) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory
Construct a new instance of SQPASEVariation for an SQP problem.
nExogenousFactors() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the number of factors, excluding the intercept.
next() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
 
next() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
Gets the next random element from the range of the probability distribution.
next() - Method in class dev.nm.stat.hmm.HMMRNG
Gets the next simulated innovation: state and observation.
next() - Method in interface dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator.Generator
Returns the next value in the underlying generated sequence.
next() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator
Returns the next value in the generated sequence.
next() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
Gets the next forecast.
next() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Gets the next forecast.
next(double) - Method in class dev.nm.stat.dlm.univariate.DLMSim
Get the next innovation.
next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast
Gets the next n-step forecasts.
next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast
Gets the next n-step forecasts.
next(int, UnivariateRealFunction, double, double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator
Compute a refined sum for the integral.
next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes
 
next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
 
next(Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim
Gets the next innovation.
nextDouble() - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
 
nextDouble() - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC
 
nextDouble() - Method in class dev.nm.stat.evt.timeseries.MARMASim
 
nextDouble() - Method in class dev.nm.stat.hmm.HMMRNG
Gets the next simulated observation.
nextDouble() - Method in class dev.nm.stat.markovchain.SimpleMC
Gets the next simulated state.
nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
 
nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
Get the next random double, which is either 1 (success) or 0 (failure).
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
Deprecated.
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
 
nextDouble() - Method in interface dev.nm.stat.random.rng.univariate.RandomNumberGenerator
Get the next random double.
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.ThinRNG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
 
nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
 
nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
 
nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
 
nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
 
nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
 
nextDouble() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Gets the next simulated observation.
nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
 
nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
 
nextLogTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
Performs a Bernoulli trial that succeeds with probability ep.
nextLogTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
Performs a Bernoulli trial that succeeds with probability ep.
nextLong() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
 
nextLong() - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
 
nextLong() - Method in interface dev.nm.stat.random.rng.univariate.RandomLongGenerator
Get the next random long.
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
All built-in linear random number generators in this library ultimately call this function to generate random numbers.
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
 
nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
 
nextMatrix() - Method in class dev.nm.stat.test.distribution.pearson.AS159
Constructs a random matrix based on the row and column sums.
nextN(RandomVectorGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
Generates n random vectors from a given random vector generator.
nextN(RandomNumberGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils
Generates n random numbers from a given random number generator.
nextP(double, double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the evolution of pt, the conditional probability of being in an uptrend given all information, i.e., \(p_t = P(\alpha_t = 1 | \mathcal{F}_t)\).
nextPair() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
Get the next random (e2_t, h_t).
nextProposal(Vector, double) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.AnnealingFunction
Gets the next proposal, given the current state and the temperature.
nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction
 
nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction
 
nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
 
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC
 
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC
 
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
Proposes a next state for the system.
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis
 
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings
 
nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
 
nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
 
nextRealization() - Method in interface dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationGenerator
Construct a realization of a multivariate stochastic process.
nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
 
nextRealization() - Method in interface dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationGenerator
Construct a realization of a univariate stochastic process.
nextRealization() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
 
nextState() - Method in class dev.nm.stat.markovchain.SimpleMC
Gets the next simulated state.
nextTime() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Get the next time point in the time grid.
nextTime() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
Get the next time point in the time grid.
nextTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
Performs a Bernoulli trial that succeeds with probability p.
nextTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
Performs a Bernoulli trial that succeeds with probability p.
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
nextVector() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.IID
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG
 
nextVector() - Method in interface dev.nm.stat.random.rng.multivariate.RandomVectorGenerator
Gets the next random vector.
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG
 
nextVector() - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
 
nextVector() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
 
nextVector() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
 
nextWeekDay(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Gets the next weekday, i.e., skipping Saturdays and Sundays.
nextX(double, double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the evolution of xt, logit of the conditional probability from (0, 1) onto \((-\infty, +\infty)\), i.e., \(x_t = \log{\frac{p_t}{1-p_t}}\).
nFactors() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the number of factors.
nFactors() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the number of variables in the original data.
nFactors() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the number of factors, including the intercept if any.
nGreaterThanInequalities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
Get the number of greater-than-or-equal-to constraints.
nGroups() - Method in class dev.nm.stat.test.HypothesisTest
Get the number of groups of observations.
nidx - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of unique values in dis_G_list
NIL - tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
 
nlshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Gets nonlinear shrinkage tau in ascending order.
NMSAAM - Class in tech.nmfin.portfoliooptimization.nmsaam
 
NMSAAM(double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
NMSAAM(double, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
NMSAAM(double, double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
NMSAAM(double, double, int, int, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
NMSAAM(double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM
 
nNoChanges - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
nNonZeros() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseStructure
Get the number of non-zero entries in the structure.
nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
no(double) - Method in interface dev.nm.number.DoubleUtils.ifelse
Return value for a false element of test.
NO_CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
This is trend type I: no constant, no linear trend:
NO_CONSTANT - dev.nm.stat.test.timeseries.adf.TrendType
test for a unit root without drift or time trend
nObs() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the number of observations.
nObs() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the number of observations in the original data; sample size.
nObs() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the number of observations.
nObs() - Method in class dev.nm.stat.test.HypothesisTest
Get the total number of observations.
NoChangeOfVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This is a dummy substitution rule that does not change any variable.
NoChangeOfVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
Construct an NoChangeOfVariable substitution rule.
NoConstraints - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
Weights are unconstrained and NoConstraints.constraints() returns null.
NoConstraints() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoConstraints
 
Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.BFS.Node
Constructs a node for a spanning tree.
Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.DFS.Node
Constructs a node for a spanning tree.
Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
Constructs a node for a spanning tree.
NoModelFitted() - Constructor for exception tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter.NoModelFitted
 
NON_BASIC - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
the non-basic variables, i.e., original variables in the cost function
noNaN(double[]) - Static method in class dev.nm.number.DoubleUtils
Remove the NaN from an array.
NonlinearFit - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess.
NonlinearFit() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit
 
NonlinearFit.Result - Class in dev.nm.stat.evt.evd.univariate.fitting.acer
 
NonlinearShrinkageEstimator - Class in dev.nm.stat.covariance.nlshrink
The nonlinear shrinkage method for given population eigenvalues.
NonlinearShrinkageEstimator(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
 
NonlinearShrinkageEstimator(QuEST.Result) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
 
nonlinearShrunkEigenvalues() - Method in class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator
Gets the nonlinear shrinkage eigenvalues in ascending order.
NonNegativityConstraintOptimProblem - Class in dev.nm.solver.multivariate.constrained.problem
This is a constrained optimization problem for a function which has all non-negative variables.
NonNegativityConstraintOptimProblem(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem
Construct a constrained optimization problem with only non-negative variables.
NonNegativityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
These constraints ensures that for all variables are non-negative.
NonNegativityConstraints(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.NonNegativityConstraints
Construct a lower bound constraints for all variables in a function.
NoPairFoundException - Exception in tech.nmfin.meanreversion.cointegration
 
NoPairFoundException(String) - Constructor for exception tech.nmfin.meanreversion.cointegration.NoPairFoundException
 
norm() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
norm() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
norm() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
norm() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
norm() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Compute the length or magnitude or Euclidean norm of a vector, namely, \(\|v\|\).
norm() - Method in interface dev.nm.algebra.structure.BanachSpace
|⋅| : B → F
norm(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
norm(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Gets the \(L^p\)-norm \(\|v\|_p\) of this vector.
norm(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the norm of a vector.
norm(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the norm of a vector.
NormalDistribution - Class in dev.nm.stat.distribution.univariate
The Normal distribution has its density a Gaussian function.
NormalDistribution() - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution
Construct an instance of the standard Normal distribution with mean 0 and standard deviation 1.
NormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution
Construct a Normal distribution with mean mu and standard deviation sigma.
NormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Normal distribution to model the observations.
NormalMixtureDistribution(NormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
Constructs a Normal distribution for each state in the HMM model.
NormalMixtureDistribution(NormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution
Constructs a Normal distribution for each state in the HMM model.
NormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution
the Normal distribution parameters
NormalOfExpFamily1 - Class in dev.nm.stat.distribution.univariate.exponentialfamily
Normal distribution, univariate, unknown mean, known variance.
NormalOfExpFamily1(double) - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily1
 
NormalOfExpFamily2 - Class in dev.nm.stat.distribution.univariate.exponentialfamily
Normal distribution, univariate, unknown mean, unknown variance.
NormalOfExpFamily2() - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily2
 
NormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
This is a random number generator that generates random deviates according to the Normal distribution.
NormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.NormalRNG
Construct a random number generator to sample from the Normal distribution.
NormalRNG(double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.NormalRNG
Construct a random number generator to sample from the Normal distribution.
NormalRVG - Class in dev.nm.stat.random.rng.multivariate
A multivariate Normal random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution.
NormalRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
Constructs a standard multivariate Normal random vector generator.
NormalRVG(Vector, Matrix) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
Constructs a multivariate Normal random vector generator.
NormalRVG(Vector, Matrix, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
Constructs a multivariate Normal random vector generator.
NormalRVG(Vector, Matrix, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG
Constructs a multivariate Normal random vector generator.
NoRootFoundException - Exception in dev.nm.analysis.root.univariate
This is the Exception thrown when it fails to find a root.
NoRootFoundException(double, double) - Constructor for exception dev.nm.analysis.root.univariate.NoRootFoundException
Construct a NoRootFoundException.
NoShortSelling - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint
Weights cannot be negative.
NoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoShortSelling
Constructs the constraint with the number of assets in the portfolio.
NoSolution(String) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.NoSolution
Construct an LinearSystemSolver.NoSolution exception.
notAKnot() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline
Constructs an instance with end conditions which fits not-a-knot splines, meaning that continuity of the third derivative at the second and the next-to-last knots are forced.
nParams() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the number of parameters for the estimation/fitting.
NPEBPortfolioMomentsEstimator - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb
Uses Non-Parametric Empirical Bayes (NPEB) approach to estimate the first and the second moments of the weighted portfolios.
NPEBPortfolioMomentsEstimator(Matrix, ReturnsMoments.Estimator, MVOptimizer, ReturnsResamplerFactory, int) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
 
nPopulation() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Get the size of the population pool, that is the number of chromosomes.
nquant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of points we select between in F[i]
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
nRows() - Method in class dev.nm.misc.datastructure.FlexibleTable
 
nRows() - Method in interface dev.nm.misc.datastructure.Table
Gets the number of rows.
nSamples() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Get the number of samples in the empirical distribution.
nSim - Variable in class dev.nm.stat.test.distribution.normality.JarqueBera
 
nStableIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
nStates() - Method in class dev.nm.stat.markovchain.SimpleMC
Gets the number of states.
nSymbols() - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM
Gets the number of observation symbols per state.
nullDeviance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals
Gets the null deviance.
nullity() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the nullity of A.
nullity(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Deprecated.
Not supported yet.
NullMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor
This IterationMonitor does nothing when a new iterate is added.
NullMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.NullMonitor
 
numberOfChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
Gets the number of children.
numberOfClusters() - Method in class dev.nm.graph.community.GirvanNewman
Gets the number of connected clusters.
numberOfEdges(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
Gets the number of edges in this graph.
numberOfParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils
Gets the number of parents.
numberOfVertices(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils
Gets the number of vertices in this graph.
NumberUtils - Class in dev.nm.number
These are the utility functions to manipulate Numbers.
NumberUtils.Comparable<T extends Number> - Interface in dev.nm.number
We need a precision parameter to determine whether two numbers are close enough to be treated as equal.
NUMERICAL - dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
use the finite difference method
numint - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of intervals
nVariables() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the number of variables in the original data set.

O

ObjectResampler<X> - Interface in dev.nm.stat.random.sampler.resampler
This is the interface of a re-sampler method for objects.
objects() - Method in class dev.nm.combinatorics.Ties
Get the set of objects counted.
observation - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation
the simulated observation
observation - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation
the simulated observation
observation() - Method in class dev.nm.stat.hmm.HmmInnovation
Get the observation associated with the hidden state.
ObservationEquation - Class in dev.nm.stat.dlm.univariate
This is the observation equation in a controlled dynamic linear model.
ObservationEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
Construct a time-invariant an observation equation.
ObservationEquation(double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
Construct a time-invariant an observation equation.
ObservationEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
Construct an observation equation.
ObservationEquation(UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
Construct an observation equation.
ObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation
Copy constructor.
ODE - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
An ordinary differential equation (ODE) is an equation in which there is only one independent variable and one or more derivatives of a dependent variable with respect to the independent variable, so that all the derivatives occurring in the equation are ordinary derivatives.
ODE(RealScalarFunction, double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
Construct an ODE of order n together with its initial values.
ODE1stOrder - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
A first order ordinary differential equation (ODE) initial value problem (IVP) takes the following form.
ODE1stOrder(DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Constructs a first order ODE with the given vector-valued function and its initial values.
ODE1stOrder(ODE) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Reduces a high order ODE to a system of first order ODEs.
ODE1stOrder(RealScalarFunction[], double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Constructs a system of first order ODEs {Yi} with their initial values {yi0}.
ODE1stOrder(RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Constructs a first order ODE with the given vector-valued function and its initial values.
ODE1stOrderWith2ndDerivative - Class in dev.nm.analysis.differentialequation.ode.ivp.problem
Some ODE solvers require the second derivative for more accurate Taylor series approximation.
ODE1stOrderWith2ndDerivative(DerivativeFunction, DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
Constructs a first order ODE with initial values.
ODE1stOrderWith2ndDerivative(RealVectorFunction, RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative
Constructs a first order ODE with initial values.
ODEIntegrator - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver
This defines the interface for the numerical integration of a first order ODE, for a sequence of pre-defined steps.
ODESolution - Class in dev.nm.analysis.differentialequation.ode.ivp.solver
Solution to an ODE problem.
ODESolution(double[], Vector[]) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
Create a solution with estimated values at the given points.
ODESolver - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver
of(LocalDate, LocalDate) - Static method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
of(LocalDateTime, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
Creates an interval from a start and end time.
of(LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
Creates an interval from a start time and a period.
of(Period, LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
Creates an interval from a period and an end time.
OLSBeta - Class in dev.nm.stat.regression.linear.ols
Beta coefficient estimator, β^, of an Ordinary Least Square linear regression model.
OLSBeta(Vector, OLSResiduals) - Constructor for class dev.nm.stat.regression.linear.ols.OLSBeta
Constructs an instance of Beta.
OLSRegression - Class in dev.nm.stat.regression.linear.ols
(Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model.
OLSRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression
Constructs an OLSRegression instance.
OLSRegression(LMProblem, double) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression
Constructs an OLSRegression instance.
OLSResiduals - Class in dev.nm.stat.regression.linear.ols
This is the residual analysis of the results of an ordinary linear regression model.
OLSResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.ols.OLSResiduals
Performs the residual analysis for an ordinary linear regression problem.
OLSSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
This class solves an over-determined system of linear equations in the ordinary least square sense.
OLSSolver(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolver
Construct an OLS solver for an over-determined system of linear equations.
OLSSolverByQR - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
This class solves an over-determined system of linear equations in the ordinary least square sense.
OLSSolverByQR(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR
Construct an OLS solver for an over-determined system of linear equations.
OLSSolverBySVD - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
This class solves an over-determined system of linear equations in the ordinary least square sense.
OLSSolverBySVD(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD
Construct an OLS solver for an over-determined system of linear equations.
ONE - Static variable in class dev.nm.analysis.function.polynomial.Polynomial
a polynomial representing 1
ONE - Static variable in class dev.nm.number.complex.Complex
a number representing 1.0 + 0.0i
ONE - Static variable in class dev.nm.number.Real
a number representing 1
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
ONE() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
Get an identity matrix that has the same dimension as this matrix.
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
ONE() - Method in interface dev.nm.algebra.structure.Monoid
The multiplicative element 1 in the group such that for any elements a in the group, the equation 1 × a = a × 1 = a holds.
ONE() - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
ONE() - Method in class dev.nm.number.complex.Complex
Get one - the number representing 1.0 + 0.0i.
ONE() - Method in class dev.nm.number.Real
 
ONE_SAMPLE - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
the one-sample Kolmogorov-Smirnov test
OneDimensionTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime
This class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate).
OneDimensionTimeSeries(MultivariateTimeSeries<T, ? extends MultivariateTimeSeries.Entry<T>>, int) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
Construct a univariate realization from a multivariate realization by taking one of its dimension (coordinate).
ones(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a matrix of 1's.
ones(int, int, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a matrix of the same scalar, e.g.,1.
oneSidedPvalue(ProbabilityDistribution, double) - Static method in class dev.nm.stat.test.HypothesisTest
The one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed.
OneWayANOVA - Class in dev.nm.stat.test.mean
The One-Way ANOVA test tests for the equality of the means of several groups.
OneWayANOVA(double[]...) - Constructor for class dev.nm.stat.test.mean.OneWayANOVA
Perform the one-way ANOVA test to test for the equality of the means of several groups.
OnlineInterpolator - Interface in dev.nm.analysis.curvefit.interpolation
An online interpolator allows dynamically adding more points for interpolation.
OPEN - dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
The first and the last terms in the Euler-Maclaurin formula are not included in the sum.
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
opposite() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
Get the opposite of this matrix.
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
opposite() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
opposite() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
opposite() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
opposite() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Get the opposite of this vector.
opposite() - Method in interface dev.nm.algebra.structure.AbelianGroup
For each a in G, there exists an element b in G such that a + b = b + a = 0.
opposite() - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
opposite() - Method in class dev.nm.number.complex.Complex
 
opposite() - Method in class dev.nm.number.Real
 
opposite(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Multiples a vector by -1, element-by-element.
optimalModelByAIC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
Selects the optimal ARIMA model by AIC.
optimalModelByAICC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit
Selects the optimal ARIMA model by AICC.
optimalWeights(Matrix, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel
Computes the weights based on given historical returns and the risk-aversion index λ.
optimalWeights(Vector, Matrix, double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizer
Solves for the optimal weights given the moments, lambda, and eta.
optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly
 
optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
 
optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint
 
optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean
 
OptimalWeights(Vector, Ceta.PortfolioMoments) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
 
Optimizer<P,​S> - Interface in dev.nm.solver
Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.
OptimProblem - Interface in dev.nm.solver.problem
This is an optimization problem that minimizes a real valued objective function, one or multi dimension.
order() - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
Get the order of this predictor-corrector pair.
order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
 
order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
 
order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
 
order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
 
order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
 
order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
 
order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
 
order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
 
order() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator
Get the order of recursion.
order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
 
order(double[]) - Static method in class dev.nm.number.DoubleUtils
Returns a permutation which rearranges its first argument into ascending or descending order.
order(double[], boolean) - Static method in class dev.nm.number.DoubleUtils
Returns a permutation which rearranges its first argument into ascending or descending order.
OrderedPairs - Interface in dev.nm.analysis.function.tuple
Cartesian products and binary relations (and hence the ubiquitous functions) are defined in terms of ordered pairs.
OrderStatisticsDistribution - Class in dev.nm.stat.evt.evd.univariate
The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.
OrderStatisticsDistribution(ProbabilityDistribution, int, int) - Constructor for class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
Create an instance with the probability distribution of \(X\), the number of iid samples to be drawn, and the order statistic.
OrnsteinUhlenbeckProcess - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
This class represents a univariate Ornstein-Uhlenbeck (OU) process.
OrnsteinUhlenbeckProcess(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
Construct a univariate OU process with unit volatility.
OrnsteinUhlenbeckProcess(double, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
Construct a univariate OU process.
OrnsteinUhlenbeckProcess(OrnsteinUhlenbeckProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
Copy constructor.
OrStopConditions - Class in dev.nm.misc.algorithm.stopcondition
Combines an arbitrary number of stop conditions, terminating when the first condition is met.
OrStopConditions(StopCondition...) - Constructor for class dev.nm.misc.algorithm.stopcondition.OrStopConditions
 
OrthogonalPolynomialFamily - Interface in dev.nm.analysis.integration.univariate.riemann.gaussian.rule
This factory class produces a family of orthogonal polynomials.
OUFitting - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
This interface defines an estimation procedure to fit a univariate Ornstein-Uhlenbeck process.
OUFittingMLE - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
This class fits a univariate Ornstein-Uhlenbeck process by using MLE.
OUFittingMLE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
Create an instance that estimates the volatility parameter σ.
OUFittingMLE(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE
Create an instance with the option whether to estimate the volatility parameter.
OUFittingOLS - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
This class fits a univariate Ornstein-Uhlenbeck process by using least squares regression.
OUFittingOLS() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
Create an instance that estimates the volatility parameter σ.
OUFittingOLS(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS
Create an instance with the option whether to estimate the volatility parameter.
OUProcess - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
 
OUSim - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou
This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process.
OUSim(OUProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Create an OU process simulator with a time interval of 1, using the overall mean as the starting value.
OUSim(OUProcess, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Create an OU process simulator using the overall mean as the starting value.
OUSim(OUProcess, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Create an OU process simulator.
OUSim(OUProcess, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
Create an OU process simulator.
outcome() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
Gets the outcome.
OuterProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation
The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.
OuterProduct(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.OuterProduct
 
outgoingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
outgoingArcs(V) - Method in interface dev.nm.graph.DiGraph
Gets the set of all outgoing arcs associated with a vertex in this graph.
outgoingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph
 
outgoingArcs(V) - Method in class dev.nm.graph.type.SparseTree
 
overdispersion() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals
Gets the over-dispersion.
overdispersion() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMResiduals
Computes the over-dispersion.
overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
overdispersion(Vector, Vector, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
Over-dispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on the nominal variance of a given simple statistical model.
overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
OVERLAP - dev.nm.interval.IntervalRelation
X overlaps with Y.
OVERLAP_INVERSE - dev.nm.interval.IntervalRelation
Y overlaps with X.
overlaps(LocalDateTimeInterval) - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
Whether this time interval overlaps with the given time interval.

P

p - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
p - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of variables
p - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
the success probability in each trial
p - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
p() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the number of interior y-axis grid points in the solution.
p() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
 
p() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
Get p, the shape parameter.
p() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Gets the dimension of the system, i.e., p = the dimension of y, the number of variables.
p() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Gets the size of b.
p() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
Get the number of AR terms.
p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
Gets the momentum.
p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Gets the current momentum.
p() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the number of AR terms.
p() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the order of the VECM model.
p() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the number of AR terms.
p() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the number of GARCH terms.
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Get the permutation matrix, P, such that P * A = L * U.
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
 
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
 
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Gets P, the pivoting matrix in the QR decomposition.
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get P, the pivoting matrix in the QR decomposition.
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
 
P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
 
P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
Get the permutation matrix P as in P * A = L * U.
P11213 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P1279 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P19937 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P21701 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P2203 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P2281 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P23209 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P3217 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P4253 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P4423 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P44497 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P521 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P607 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P9689 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
P9941 - dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
 
Package - Class in dev.nm.misc.license
 
Pair - Class in dev.nm.analysis.function.tuple
An ordered pair (x,y) is a pair of mathematical objects.
Pair(double, double) - Constructor for class dev.nm.analysis.function.tuple.Pair
Construct a pair.
PairComparatorByAbscissaFirst - Class in dev.nm.analysis.function.tuple
 
PairComparatorByAbscissaFirst() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
 
PairComparatorByAbscissaFirst(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst
 
PairComparatorByAbscissaOnly - Class in dev.nm.analysis.function.tuple
 
PairComparatorByAbscissaOnly() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
 
PairComparatorByAbscissaOnly(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly
 
PairingCheck - Interface in tech.nmfin.meanreversion.cointegration.check
 
PairingModel - Interface in tech.nmfin.meanreversion.cointegration
Given a set of symbols, their prices and other information, we find mean reverting pairs for trading.
PairingModel1 - Class in tech.nmfin.meanreversion.cointegration
 
PairingModel1(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel1
 
PairingModel2 - Class in tech.nmfin.meanreversion.cointegration
 
PairingModel2(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel2
 
PairingModel3 - Class in tech.nmfin.meanreversion.cointegration
 
PairingModel3(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel3
 
PairingModel4 - Class in tech.nmfin.meanreversion.cointegration
 
PairingModel4(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel4
 
PairingModel5 - Class in tech.nmfin.meanreversion.cointegration
 
PairingModel5(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel5
 
PairingModelUtils - Class in tech.nmfin.meanreversion.cointegration
 
PairingModelUtils() - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModelUtils
 
PanelData<S,​T extends Comparable<? super T>> - Class in dev.nm.stat.regression.linear.panel
A panel data refers to multi-dimensional data frequently involving measurements over time.
PanelData(String, String, String[]) - Constructor for class dev.nm.stat.regression.linear.panel.PanelData
Constructs a panel of two-dimensional data.
PanelData.Row - Class in dev.nm.stat.regression.linear.panel
This is one row of the data in a panel.
PanelData.Transformation - Interface in dev.nm.stat.regression.linear.panel
Transforms the data, e.g., taking log.
PanelRegression - Interface in dev.nm.stat.regression.linear.panel
Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two-dimensional (cross sectional/times series) panel data.
parallel - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
This indicate if the algorithm is to run in parallel (multi-core).
ParallelDoubleArrayOperation - Class in dev.nm.number.doublearray
This is a multi-threaded implementation of the array math operations.
ParallelDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
ParallelExecutor - Class in dev.nm.misc.parallel
This class provides a framework for executing an algorithm in parallel.
ParallelExecutor() - Constructor for class dev.nm.misc.parallel.ParallelExecutor
Creates an instance using default concurrency number, which is the number of available processors returned by
ParallelExecutor(int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
Creates an instance with a specified concurrency number.
ParallelExecutor(String) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
Creates an instance with a specified executor name.
ParallelExecutor(String, int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor
Creates an instance with a specified concurrency number, and a name of the executor.
parent() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
Gets the parent of the node.
parent(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
parent(V) - Method in interface dev.nm.graph.Tree
Gets the unique parent of a vertex.
parent(V) - Method in class dev.nm.graph.type.SparseTree
 
parents(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
parents(V) - Method in interface dev.nm.graph.DiGraph
Gets the set of all parents of this vertex.
parents(V) - Method in class dev.nm.graph.type.SparseDiGraph
 
parents(V) - Method in class dev.nm.graph.type.SparseTree
 
parse(String) - Static method in class dev.nm.number.NumberUtils
Construct a number from a String.
parseArray(String...) - Static method in class dev.nm.number.NumberUtils
Convert an array of numbers in String to an array of numbers in Number.
PartialDerivativesByCenteredDifferencing - Class in dev.nm.analysis.curvefit.interpolation.bivariate
This implementation computes the partial derivatives by centered differencing.
PartialDerivativesByCenteredDifferencing() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing
 
PartialFunction - Class in dev.nm.analysis.function.tuple
A partial function from X to Y is a function f: X' → Y, where X' is a subset of X.
PartialFunction(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.PartialFunction
Construct a partial function from {(x,y)}.
PAST - dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
Use only past values.
paste(Collection<String>, String) - Static method in class dev.nm.misc.StringUtils
Concatenates Strings into one String.
path - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
PattonPolitisWhite2009 - Class in dev.nm.stat.random.sampler.resampler.bootstrap.block
This class implements the stationary and circular block bootstrapping method with optimized block length.
PattonPolitisWhite2009(double[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject<X> - Class in dev.nm.stat.random.sampler.resampler.bootstrap.block
This class implements the stationary and circular block bootstrapping method with optimized block length.
PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
Constructs a block bootstrap sample generator.
PattonPolitisWhite2009ForObject.AutoCorrelationForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block
 
PattonPolitisWhite2009ForObject.AutoCovarianceForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block
 
PattonPolitisWhite2009ForObject.Type - Enum in dev.nm.stat.random.sampler.resampler.bootstrap.block
 
PCA - Interface in dev.nm.stat.factor.pca
Principal Component Analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.
PCAbyEigen - Class in dev.nm.stat.factor.pca
This class performs Principal Component Analysis (PCA) on a data matrix, using eigen decomposition on the correlation or covariance matrix.
PCAbyEigen(Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen
Performs Principal Component Analysis, using the eigen method and using correlation matrix, on a data matrix.
PCAbyEigen(Matrix, boolean) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen
Performs Principal Component Analysis, using the eigen method, on a data matrix.
PCAbyEigen(Matrix, boolean, Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen
Performs Principal Component Analysis, using the eigen method, on a data matrix with an optional correlation (or covariance) matrix provided.
PCAbySVD - Class in dev.nm.stat.factor.pca
This class performs Principal Component Analysis (PCA) on a data matrix, using the preferred Singular Value Decomposition (SVD) method.
PCAbySVD(Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD
Performs Principal Component Analysis, using the preferred SVD method, on a centered and scaled data matrix.
PCAbySVD(Matrix, boolean, boolean) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD
Performs Principal Component Analysis, using the preferred SVD method, on a data matrix (possibly centered and/or scaled).
PCAbySVD(Matrix, Vector, Vector) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD
Performs Principal Component Analysis, using the preferred SVD method, on a data matrix with (optional) mean vector and scaling vector provided.
PDE - Interface in dev.nm.analysis.differentialequation.pde
A partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives.
PDESolutionGrid2D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference
A solution to a bivariate PDE, which is applicable to methods which produce the solution as a two-dimensional grid.
PDESolutionTimeSpaceGrid1D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference
A solution to an one-dimensional PDE, which is applicable to methods which produce the solution as a grid of time and space.
PDESolutionTimeSpaceGrid2D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference
A solution to a two-dimensional PDE, which is applicable to methods which produce the solution as a three-dimensional grid of time and space.
PDESolver - Interface in dev.nm.analysis.differentialequation.pde
A PDE solver solves a set of PDEs.
PDETimeSpaceGrid1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference
This grid numerically solves a 1D PDE, e.g., using the Crank-Nicolson scheme.
PDETimeSpaceGrid1D(int) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
Constructs a time-space grid.
PeaksOverThreshold - Class in dev.nm.stat.evt.evd.univariate.fitting.pot
Peaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution (GPD) using maximum likelihood on the observations that are over a given threshold.
PeaksOverThreshold(double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThreshold
Create an instance for POT method with a given threshold.
PeaksOverThresholdOnClusters - Class in dev.nm.stat.evt.evd.univariate.fitting.pot
Similar to POT, but only use the peak observations in clusters for the parametric estimation.
PeaksOverThresholdOnClusters(ClusterAnalyzer) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters
Create an instance with a ClusterAnalyzer which is used to find clusters from observations.
PearsonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
This is the Pearson method.
PearsonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.PearsonMinimizer
Construct a multivariate minimizer using the Pearson method.
pearsonStat(Matrix, Matrix, boolean) - Static method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
Compute the Pearson's cumulative test statistic, which asymptotically approaches a χ2 distribution.
penalizedCardinality(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Gets the value of a cardinality-penalized function.
penalizedL1(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Gets the value of an L1-penalized function.
PenaltyFunction - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties.
PenaltyFunction() - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyFunction
 
PenaltyMethodMinimizer - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
The penalty method is an algorithm for solving a constrained minimization problem with general constraints.
PenaltyMethodMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
Construct a constrained minimizer using the penalty method.
PenaltyMethodMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
Construct a constrained minimizer using the penalty method.
PenaltyMethodMinimizer(PenaltyMethodMinimizer.PenaltyFunctionFactory, double, IterativeC2Minimizer) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
Construct a constrained minimizer using the penalty method.
PenaltyMethodMinimizer.PenaltyFunctionFactory - Interface in dev.nm.solver.multivariate.constrained.general.penaltymethod
For each constrained optimization problem, the solver creates a new penalty function for it.
periodCountBetween(LocalDateTime, LocalDateTime, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Returns the number of whole periods between two time instants.
periodDayCount(Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Estimates the number of days in a period, with the assumed number of days per month.
periodicInstants(LocalDateTime, Period, int) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Generates a list of periodic time instants.
periodicInstantsSpanningInterval(LocalDateTimeInterval, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Generates a list of periodic time instants which span the whole given interval.
periodicInstantsWithinInterval(LocalDateTimeInterval, Period) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Generates a list of periodic time instants within a given interval.
permutation(int, int) - Static method in class dev.nm.analysis.function.FunctionOps
Compute the permutation function.
permutation(int, int) - Static method in class dev.nm.number.big.BigIntegerUtils
Compute the permutation function.
PermutationMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype
A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.
PermutationMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Construct an identity permutation matrix.
PermutationMatrix(int[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Construct a permutation matrix from an 1D double[].
PermutationMatrix(PermutationMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Copy constructor.
perpendicularDistance(Point) - Method in class dev.nm.geometry.LineSegment
 
PerturbationAroundPoint - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
The initial population is generated by adding a variance around a given initial.
PerturbationAroundPoint(RealScalarFunction, SimpleCellFactory, int, Vector, Vector, long) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint
Generate an initial pool of chromosomes by adding a variance around a given initial.
phi - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
phi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get all the AR coefficients.
phi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get all the AR coefficients.
phiPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the polynomial (1 - φ).
PhysicalConstants - Class in dev.nm.misc
A collection of fundamental physical constants.
pi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the impact matrix.
PI - Static variable in class dev.nm.number.big.BigDecimalUtils
the value of PI
PI() - Method in class dev.nm.stat.markovchain.SimpleMC
Gets the initial state probabilities.
PI() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
Get the coefficients of the linear representation of the time series.
PI_SQ - Static variable in class dev.nm.misc.Constants
\(\pi^2\)
Pivot(int, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
Construct a pivot.
PLANCK_H - Static variable in class dev.nm.misc.PhysicalConstants
The Planck constant \(h\) in joule seconds (J s).
PLANCK_REDUCED_HBAR - Static variable in class dev.nm.misc.PhysicalConstants
The reduced Planck constant (or Dirac constant) \(\hbar\), defined as the Planck constant divided by 2π, in joule seconds (J s).
Point - Class in dev.nm.geometry
Represent a n-dimensional point.
Point(double...) - Constructor for class dev.nm.geometry.Point
Create a point with given coordinates.
Point(Vector) - Constructor for class dev.nm.geometry.Point
Create a point with given coordinates.
points(double, double) - Method in interface dev.nm.root.univariate.GridSearchMinimizer.GridDefinition
 
PoissonDistribution - Class in dev.nm.stat.distribution.univariate
The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.
PoissonDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.PoissonDistribution
Construct a Poisson distribution.
PoissonEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2
Poisson's equation is an elliptic PDE that takes the following general form.
PoissonEquation2D(double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D
Constructs a Poisson's equation problem.
PoissonMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution
The HMM states use the Poisson distribution to model the observations.
PoissonMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution
Constructs a Poisson distribution for each state in the HMM model.
PolygonalChain - Interface in dev.nm.geometry.polyline
A polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected series of line segments.
PolygonalChainByArray - Class in dev.nm.geometry.polyline
An immutable PolygonalChain that is backed by an ArrayList.
PolygonalChainByArray(List<? extends Point>) - Constructor for class dev.nm.geometry.polyline.PolygonalChainByArray
Create a new instance which uses the given vertices.
Polynomial - Class in dev.nm.analysis.function.polynomial
A polynomial is a UnivariateRealFunction that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.
Polynomial(double...) - Constructor for class dev.nm.analysis.function.polynomial.Polynomial
Construct a polynomial from an array of coefficients.
Polynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.Polynomial
Copy constructor.
PolyRoot - Class in dev.nm.analysis.function.polynomial.root
This is a solver for finding the roots of a polynomial equation.
PolyRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.PolyRoot
 
PolyRootSolver - Interface in dev.nm.analysis.function.polynomial.root
A root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes.
pop() - Method in interface dev.nm.misc.algorithm.bb.ActiveList
Get the next node.
population - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
This is the (current) population pool.
PortfolioMoments(double, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments
 
PortfolioOptimizationAlgorithm - Interface in tech.nmfin.portfoliooptimization
Computes the optimal weights based only on returns.
PortfolioOptimizationAlgorithm.CovarianceEstimator - Interface in tech.nmfin.portfoliooptimization
Define how the expected covariances of an asset for a future period is computed.
PortfolioOptimizationAlgorithm.MeanEstimator - Interface in tech.nmfin.portfoliooptimization
Define how the expected mean of an asset for a future period is computed.
PortfolioOptimizationAlgorithm.SampleCovarianceEstimator - Class in tech.nmfin.portfoliooptimization
Estimate the expected covariances of an asset using sample covariances.
PortfolioOptimizationAlgorithm.SampleMeanEstimator - Class in tech.nmfin.portfoliooptimization
Estimate the expected mean of an asset using sample mean.
PortfolioOptimizationAlgorithm.SymbolLookup - Interface in tech.nmfin.portfoliooptimization
Provides a lookup for product symbols and indices.
PortfolioRiskExactSigma - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Constructs the constraint coefficient arrays of the portfolio risk term in the compact form.
PortfolioRiskExactSigma(Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.
PortfolioRiskExactSigma(Matrix, Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.
PortfolioRiskExactSigma(Matrix, PortfolioRiskExactSigma.MatrixRoot) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.
PortfolioRiskExactSigma.DefaultRoot - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Computes the matrix root by Cholesky and on failure by MatrixRootByDiagonalization.
PortfolioRiskExactSigma.Diagonalization - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Computes the matrix root by MatrixRootByDiagonalization.
PortfolioRiskExactSigma.MatrixRoot - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Specifies the method to compute the root of a matrix.
PortfolioUtils - Class in tech.nmfin.portfoliooptimization
 
position() - Method in interface tech.nmfin.meanreversion.volarb.MRModel
Gets the current target position computed by this model.
position() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
 
position(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
POSITIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex
a number representing +∞ + ∞i
PositiveDefiniteMatrixByPositiveDiagonal - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.
PositiveDefiniteMatrixByPositiveDiagonal(Matrix, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveDefiniteMatrixByPositiveDiagonal
Constructs a positive definite matrix by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.
PositiveSemiDefiniteMatrixNonNegativeDiagonal - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by forcing the negative diagonal entries in the eigen decomposition to 0.
PositiveSemiDefiniteMatrixNonNegativeDiagonal(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveSemiDefiniteMatrixNonNegativeDiagonal
Constructs a positive semi-definite matrix by forcing the negative diagonal entries in the eigen decomposition to 0.
pow(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
pow(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Take the exponentiation of all entries in this vector, entry-by-entry.
pow(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Raise each element in an array to the power of the given exponent.
pow(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
pow(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Takes a power of a vector, element-by-element.
pow(Complex, Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
z1 to the power z2.
pow(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute a to the power of n, where n is an integer.
pow(BigDecimal, int, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute a to the power of n, where n is an integer.
pow(BigDecimal, BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute a to the power of b.
pow(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils
Compute a to the power of b.
Pow - Class in dev.nm.algebra.linear.matrix.doubles.operation
This is a square matrix A to the power of an integer n, An.
Pow(Matrix, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow
Construct the power matrix An so that An = (1e100)scale * B
Pow(Matrix, int, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow
Construct the power matrix An so that An = basescale * B
PowellImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl
 
PowellMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
Powell's algorithm, starting from an initial point, performs a series of line searches in one iteration.
PowellMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
Construct a multivariate minimizer using the Powell method.
PowellMinimizer.PowellImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
an implementation of Powell's algorithm
PowerLawSingularity - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation is good for an integral which diverges at one of the end points.
PowerLawSingularity(PowerLawSingularity.PowerLawSingularityType, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
Construct a PowerLawSingularity substitution rule.
PowerLawSingularity.PowerLawSingularityType - Enum in dev.nm.analysis.integration.univariate.riemann.substitution
the type of end point divergence
PrecisionUtils - Class in dev.nm.misc
Precision-related utility functions.
Preconditioner - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
Preconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergence when the system is solved by an iterative method.
PreconditionerFactory - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
This constructs a new instance of Preconditioner for a coefficient matrix.
predict(DerivativeFunction, double, double[], Vector[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector
 
predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1
 
predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2
 
predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3
 
predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4
 
predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5
 
previousWeekDay(LocalDateTime) - Static method in class dev.nm.misc.datastructure.time.LocalDateTimeUtils
Gets the previous weekday, i.e., skipping Saturdays and Sundays.
price1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
price2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
This is pivot column selection (pricing) rule.
pricing(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
This is pivot column selection (pricing) rule.
pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
This is pivot column selection (pricing) rule.
PrimalDualInteriorPointMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
Solves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.
PrimalDualInteriorPointMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer
Constructs a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.
PrimalDualInteriorPointMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This is the solution to a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.
PrimalDualInteriorPointMinimizer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
The SOCP dual problem we are solving here is : \max {\bm b}^T \hat{\bm y} \\ {\rm s.t.} ({\bm A_i^q})^T \hat{\bm y} + {\bm z_i^q} = c_i^q,\ {\bm z_i^q}\in \mathcal{K}_q^{q_i},\ for i\in [n_q];\\ ({\bm A^{\ell}})^T \hat{\bm y} + {\bm z}^{\ell} = c^{\ell},\ {\bm z}^{\ell} \ge 0;\\ ({\bm A^u})^T \hat{\bm y} = c^u;\\ \hat{\bm y} \in \mathbb{R}^m;\ {\bm z}^{\ell}\in \mathbb{R}^{n_{\ell}};\ {\bm z}^u \in \mathbb{R}^{n_u}.
PrimalDualInteriorPointMinimizer1(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1
Constructs a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.
PrimalDualInteriorPointMinimizer1.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This is the solution to a Dual Second-Order Conic Programming problem using the Primal-Dual Predictor-Corrector Interior Point algorithm.
PrimalDualPathFollowingMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems.
PrimalDualPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
PrimalDualPathFollowingMinimizer(double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
PrimalDualPathFollowingMinimizer(double, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.
PrimalDualPathFollowingMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
This is the solution to a Semi-Definite Programming problem using the Primal-Dual Path-Following algorithm.
PrimalDualSolution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
The vector set {x, s, y} is a solution to both the primal and dual SOCP problems.
PrimalDualSolution(Vector, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
Construct a solution to a primal and a dual SOCP problems.
prob - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
the probability of observing this matrix
probability() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass
Gets the probability of the outcome.
ProbabilityDistribution - Interface in dev.nm.stat.distribution.univariate
A univariate probability distribution completely characterizes a random variable by stipulating the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous).
ProbabilityMassFunction<X> - Interface in dev.nm.stat.distribution.discrete
A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.
ProbabilityMassFunction.Mass<X> - Class in dev.nm.stat.distribution.discrete
Stores a possible outcome for a probability distribution and its associated probability.
ProbabilityMassQuantile<X> - Class in dev.nm.stat.distribution.discrete
As probability mass function is discrete, there are gaps between values in the domain of its cdf, The quantile function is: \[ Q(p)\,=\,\inf\left\{ x\in R : p \le F(x) \right\} \]
ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
Constructs the quantile function for a probability mass function.
ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>, double) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
Constructs the quantile function for a probability mass function.
ProbabilityMassSampler<X> - Class in dev.nm.stat.distribution.discrete
A random sampler that is constructed ad-hoc from a list of values and their probabilities.
ProbabilityMassSampler(List<ProbabilityMassFunction.Mass<X>>, RandomLongGenerator) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
Creates an instance with the probable values and an RNG.
problem - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
problem - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
problem() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO
Get the original covariance selection problem.
problem() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Returns the original GLM problem.
Problem(Matrix, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem
 
process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1
Find a feasible table, if any.
process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2
Remove equalities and free variables, if possible.
product(GivensMatrix[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Given an array of Givens matrices {Gi}, computes G, where G = G1 * G2 * ...
product(HouseholderReflection[], int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Compute Q from Householder matrices {Qi}.
product(HouseholderReflection[], int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Compute Q from Householder matrices {Qi}.
product(List<GivensMatrix>) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
ProductOfWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
Defines portfolio diversification as \[ D(w) = \prod_i w_i \]
ProductOfWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
 
Projection - Class in dev.nm.algebra.linear.vector.doubles.operation
Project a vector v on another vector w or a set of vectors (basis) {wi}.
Projection(Vector, Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
Project a vector v onto another vector.
Projection(Vector, Vector[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
Project a vector v onto a set of basis {wi}.
Projection(Vector, List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection
Project a vector v onto a set of basis {wi}.
propagate() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
Propagates the grid to the next time step by solving \(Au=d\).
proportionVar() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the proportion of overall variance explained by each of the principal components.
proportionVar() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
 
proportionVar(int) - Method in interface dev.nm.stat.factor.pca.PCA
Gets the proportion of overall variance explained by the i-th principal component.
ProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
A proposal function goes from the current state to the next state, where a state is a vector.
ProposalFunction(int, int) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.ProposalFunction
 
PROTON_ELECTRON_MASS_RATIO - Static variable in class dev.nm.misc.PhysicalConstants
The proton-electron mass ratio \(m_p/m_e\) (dimensionless).
PROTON_MASS_MP - Static variable in class dev.nm.misc.PhysicalConstants
The proton mass \(m_p\) in kilograms (kg).
PrZt0() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Computes the stationary probability of in state Z_t = 0.
PrZt1() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
Computes the stationary probability of in state Z_t = 1.
PseudoInverse - Class in dev.nm.algebra.linear.matrix.doubles.operation
The Moore-Penrose pseudo-inverse of an m x n matrix A is A+.
PseudoInverse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse
Construct the Moore-Penrose pseudo-inverse matrix of A.
PseudoInverse(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse
Construct the Moore-Penrose pseudo-inverse matrix of a matrix.
psi() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Gets the estimated (optimal) psi, E(ee'), p.
psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the coefficients of the deterministic terms.
psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the coefficients of the deterministic terms.
psi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the coefficients of the deterministic terms.
psi(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
 
PureILPProblem - Class in dev.nm.solver.multivariate.constrained.integer.linear.problem
This is a pure integer linear programming problem, in which all variables are integral.
PureILPProblem(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.problem.PureILPProblem
Construct a pure ILP problem.
pValue() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Calculates the p-value of the test statistics, given the degree of freedom.
pValue() - Method in class dev.nm.stat.test.distribution.AndersonDarling
 
pValue() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
 
pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
 
pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
 
pValue() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
 
pValue() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
 
pValue() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
 
pValue() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
 
pValue() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
 
pValue() - Method in class dev.nm.stat.test.HypothesisTest
Get the p-value for the test statistics.
pValue() - Method in class dev.nm.stat.test.mean.OneWayANOVA
 
pValue() - Method in class dev.nm.stat.test.mean.T
 
pValue() - Method in class dev.nm.stat.test.rank.KruskalWallis
 
pValue() - Method in class dev.nm.stat.test.rank.SiegelTukey
 
pValue() - Method in class dev.nm.stat.test.rank.VanDerWaerden
 
pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
 
pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
 
pValue() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
 
pValue() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
 
pValue() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
Deprecated.
 
pValue() - Method in class dev.nm.stat.test.variance.Bartlett
 
pValue() - Method in class dev.nm.stat.test.variance.BrownForsythe
 
pValue() - Method in class dev.nm.stat.test.variance.F
 
pValue() - Method in class dev.nm.stat.test.variance.Levene
 
pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Compute the two-sided p-value for a critical value.
pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Compute the two-sided p-value for a critical value.
pValueAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling
Gets the alternative p-value (adjusted for ties).
pvalueZ1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
Get the p-value for Z1.
pvalueZ2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
Get the p-value for Z2.
pw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of each distinct eigenvalues
pzw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
number of eigenvalues that are less or equals to zero

Q

q - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
q() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized
Get q, the shape parameter.
q() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Gets the number of A matrices.
q() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
q() - Method in class dev.nm.stat.evt.timeseries.MARMAModel
Get the number of MA terms.
q() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the number of MA terms.
q() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the number of MA terms.
q() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Get the number of ARCH terms.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
Returns the rotation matrix.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
Gets Q, such that Q * A * Q = T.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
Get Q as in Q * D * Q' = A.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition
Gets the Q matrix, where \[ Q = (Q_1 \times ...
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Gets the Q matrix as in the real Schur canonical form Q'AQ = T.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Gets the Q matrix as in Q'AQ = D, where D is diagonal and Q is orthogonal.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
 
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Gets the Q matrix in the QR decomposition.
Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
Q() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get the orthogonal Q matrix in the QR decomposition, A = QR.
QPbySOCPMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
We first convert a QP problem to an equivalent SOCP problem and then solve it using an SOCP solver.
QPbySOCPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer
Constructs an SOCP minimizer to solve quadratic programming problems.
QPbySOCPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
 
QPbySOCPMinimizer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
A QP problem is first converted into an equivalent SOCP problem SOCPGeneralProblem1 and then solve it using an SOCP solver PrimalDualInteriorPointMinimizer1.
QPbySOCPMinimizer1(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1
Constructs an SOCP minimizer to solve quadratic programming problems.
QPbySOCPMinimizer1.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
 
QPConstraint - Interface in tech.nmfin.portfoliooptimization.markowitz.constraints
This interface allows adding constraints to a Quadratic Programming problem solving w_eff, the efficient frontier or the optimal allocation of assets.
QPDualActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
This implementation solves a Quadratic Programming problem using the dual active set algorithm.
QPDualActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
 
QPDualActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
 
QPDualActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
This is the solution to a Quadratic Programming problem using the Dual Active Set algorithm.
QPException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
This is the exception thrown when there is an error solving a quadratic programming problem.
QPException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPException
Construct an instance of QPException.
QPInfeasible - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
This is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i.e., no solution.
QPInfeasible() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPInfeasible
 
QPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver
A typedef for QP minimizer.
QPMinWeights - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
 
QPMinWeights(double...) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
QPMinWeights(int, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
QPMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights
 
QPNoConstraint - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
Deprecated.
This constraint means that you can borrow indefinitely, which makes very little economics meaning.
QPNoConstraint() - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint
Deprecated.
 
QPNoShortSelling - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
 
QPNoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling
 
QPPrimalActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
This implementation solves a Quadratic Programming problem using the Primal Active Set algorithm.
QPPrimalActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
Constructs a Primal Active Set minimizer to solve quadratic programming problems.
QPPrimalActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
Constructs a Primal Active Set minimizer to solve quadratic programming problems.
QPPrimalActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
This is the solution to a Quadratic Programming problem using the Primal Active Set algorithm.
QPProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
Quadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables.
QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem with linear equality and greater-than-or-equal-to constraints.
QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem.
QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem with linear equality and less-than-or-equal-to constraints.
QPProblem(QuadraticFunction, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem with linear greater-than-or-equal-to constraints.
QPProblem(QuadraticFunction, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem with linear inequality constraints.
QPProblem(QuadraticFunction, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Construct a quadratic programming problem with linear less-than-or-equal-to constraints.
QPProblem(QPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem
Copy constructor.
QPProblemOnlyEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.
QPProblemOnlyEqualityConstraints(QuadraticFunction, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints
Construct a quadratic programming problem with only equality constraints.
QPSimpleMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
These are the utility functions to solve simple quadratic programming problems that admit analytical solutions.
QPSimpleMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
 
QPSolution - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp
This is a solution to a quadratic programming problem.
QPtoSOCPTransformer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
Transforms a QPProblem to a SOCPGeneralProblem.
QPtoSOCPTransformer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer
 
QPtoSOCPTransformer1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp
Transforms a QPProblem to a SOCPGeneralProblem1.
QPtoSOCPTransformer1() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer1
 
QPUnity - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
 
QPUnity(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity
 
QPWeightsLimit - Class in tech.nmfin.portfoliooptimization.markowitz.constraints
 
QPWeightsLimit(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit
 
QR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr
QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.
QR - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
For any matrix.
QR - dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
use QR decomposition; moderately expensive (recommended)
QR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
Run the QR decomposition on a matrix.
QR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
Run the QR decomposition on a matrix.
QR_SYMMETRIC - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
For a symmetric matrix.
QRAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
The QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a matrix.
QRAlgorithm(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Runs the QR algorithm on a square matrix.
QRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Runs the QR algorithm on a square matrix.
QRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Runs the QR algorithm on a square matrix.
QRDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.qr
QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.
Qs() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
Gets the list of Qi's produced in the process of the QR algorithm (if keepQs is set to true).
Qt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition
Get Q' as in Q * D * Q' = A.
QuadraticFunction - Class in dev.nm.analysis.function.rn2r1
A quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
QuadraticFunction(Matrix, Vector) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p\).
QuadraticFunction(Matrix, Vector, double) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
QuadraticFunction(QuadraticFunction) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction
Copy constructor.
QuadraticMonomial - Class in dev.nm.analysis.function.polynomial
A quadratic monomial has this form: x2 + ux + v.
QuadraticMonomial(double, double) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticMonomial
Construct a quadratic monomial.
QuadraticRoot - Class in dev.nm.analysis.function.polynomial.root
This is a solver for finding the roots of a quadratic equation, \(ax^2 + bx + c = 0\).
QuadraticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuadraticRoot
 
QuadraticSyntheticDivision - Class in dev.nm.analysis.function.polynomial
Divide a polynomial P(x) by a quadratic monomial (x2 + ux + v) to give the quotient Q(x) and the remainder (b * (x + u) + a).
QuadraticSyntheticDivision(Polynomial, QuadraticMonomial) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
Divide a polynomial by a quadratic monomial.
quant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
point indices in F[i]; kappa index
quantile(double) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile
Gets the quantile at a cumulative probability mass.
quantile(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
Gets the quantile, the inverse of the cumulative distribution function.
quantile(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Gets the quantile, the inverse of the cumulative distribution function.
quantile(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
quantile(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the quantile, the inverse of the cumulative distribution function.
quantile(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
quantile(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
 
quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
quantile(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
Quantile - Class in dev.nm.stat.descriptive.rank
Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable.
Quantile() - Constructor for class dev.nm.stat.descriptive.rank.Quantile
Construct a Quantile calculator using the default type: Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED, without initial data.
Quantile(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Quantile
Construct a Quantile calculator using the default type: Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED.
Quantile(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.descriptive.rank.Quantile
Construct a Quantile calculator.
Quantile.QuantileType - Enum in dev.nm.stat.descriptive.rank
the available quantile definitions
QuarticRoot - Class in dev.nm.analysis.function.polynomial.root
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
QuarticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot
Construct a quartic equation solver.
QuarticRoot(QuarticRoot.QuarticSolver) - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot
Construct a quartic equation solver.
QuarticRoot.QuarticSolver - Interface in dev.nm.analysis.function.polynomial.root
This defines a quartic equation solver.
QuarticRootFerrari - Class in dev.nm.analysis.function.polynomial.root
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using the Ferrari method.
QuarticRootFerrari() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
 
QuarticRootFormula - Class in dev.nm.analysis.function.polynomial.root
This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using a root-finding formula.
QuarticRootFormula() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
 
QuasiBinomial - Class in dev.nm.stat.regression.linear.glm.quasi.family
This is the quasi Binomial distribution in GLM.
QuasiBinomial() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
 
quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
 
quasiDeviance(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution
the quasi-deviance function corresponding to a single observation
quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
 
quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
 
quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
 
quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
 
QuasiDistribution - Interface in dev.nm.stat.regression.linear.glm.quasi.family
This interface represents the quasi-distribution used in GLM.
QuasiFamily - Class in dev.nm.stat.regression.linear.glm.quasi.family
This interface represents the quasi-family used in GLM.
QuasiFamily(QuasiBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
Construct a Binomial quasi-family.
QuasiFamily(QuasiDistribution, LinkFunction) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
 
QuasiFamily(QuasiGamma) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
Construct a Gamma quasi-family.
QuasiFamily(QuasiGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
Construct a Gaussian quasi-family.
QuasiFamily(QuasiInverseGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
Construct an Inverse Gaussian quasi-family.
QuasiFamily(QuasiPoisson) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily
Construct a Poisson quasi-family.
QuasiGamma - Class in dev.nm.stat.regression.linear.glm.quasi.family
This is the quasi Gamma distribution in GLM.
QuasiGamma() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
 
QuasiGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family
This is the quasi Gaussian distribution in GLM.
QuasiGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
 
QuasiGLMBeta - Class in dev.nm.stat.regression.linear.glm.quasi
This is the estimate of beta, β^, in a quasi Generalized Linear Model, i.e., a GLM with a quasi-family of distributions.
QuasiGLMBeta(QuasiGLMNewtonRaphson, GLMResiduals) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMBeta
Construct an instance of Beta.
QuasiGLMNewtonRaphson - Class in dev.nm.stat.regression.linear.glm.quasi
The Newton-Raphson method is an iterative algorithm to estimate the β of the quasi GLM regression.
QuasiGLMNewtonRaphson(double, int) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
Constructs an instance to run the Newton-Raphson method.
QuasiGLMProblem - Class in dev.nm.stat.regression.linear.glm.quasi
This class represents a quasi generalized linear regression problem.
QuasiGLMProblem(Vector, Matrix, boolean, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
Constructs a quasi GLM problem.
QuasiGLMProblem(LMProblem, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem
Constructs a quasi GLM problem from a linear regression problem.
QuasiGLMResiduals - Class in dev.nm.stat.regression.linear.glm.quasi
Residual analysis of the results of a quasi Generalized Linear Model regression.
QuasiInverseGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family
This is the quasi Inverse-Gaussian distribution in GLM.
QuasiInverseGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
 
quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial
 
quasiLikelihood(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution
the quasi-likelihood function corresponding to a single observation Q(μ; y)
quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma
 
quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian
 
quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian
 
quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
 
QuasiMinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system.
QuasiMinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
Construct a Quasi-Minimal Residual (QMR) solver.
QuasiMinimalResidualSolver(PreconditionerFactory, PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
Construct a Quasi-Minimal Residual (QMR) solver.
QuasiNewtonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
The Quasi-Newton methods in optimization are for finding local maxima and minima of functions.
QuasiNewtonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer
Construct a multivariate minimizer using a Quasi-Newton method.
QuasiNewtonMinimizer.QuasiNewtonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
This is an implementation of the Quasi-Newton algorithm.
QuasiPoisson - Class in dev.nm.stat.regression.linear.glm.quasi.family
This is the quasi Poisson distribution in GLM.
QuasiPoisson() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson
 
QuEST - Class in dev.nm.stat.covariance.nlshrink.quest
QuEST is a function that generates sample eigenvalues from population eigenvalues.
QuEST(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST
 
QuEST(Vector, int, double, double) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST
 
QuEST.Result - Class in dev.nm.stat.covariance.nlshrink.quest
 
quotient() - Method in class dev.nm.analysis.function.polynomial.HornerScheme
Get the quotient, Q(x).
quotient() - Method in class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision
Get the quotient Q(x).

R

r - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
the pivot row
r - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
r(CointegrationMLE, double) - Method in class dev.nm.stat.cointegration.JohansenTest
Get the (most likely) order of cointegration.
R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
 
R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
 
R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
R() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get the upper triangular matrix R in the QR decomposition, A = QR.
R(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the source (or sink) value at a given time t and a position x.
R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result
Gets R_bar, the average of observations over time per subject.
R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result
Gets R_bar, the average of observations over time per subject.
R1Projection - Class in dev.nm.analysis.function.rn2r1
Projection creates a real-valued function RealScalarFunction from a vector-valued function RealVectorFunction by taking only one of its coordinate components in the vector output.
R1Projection(RealVectorFunction, int) - Constructor for class dev.nm.analysis.function.rn2r1.R1Projection
Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.
R1toConstantMatrix - Class in dev.nm.analysis.function.matrix
A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).
R1toConstantMatrix(Matrix) - Constructor for class dev.nm.analysis.function.matrix.R1toConstantMatrix
Construct a constant matrix function.
R1toMatrix - Class in dev.nm.analysis.function.matrix
This is a function that maps from R1 to a Matrix space.
R1toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R1toMatrix
 
R2() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: R-squared.
R2toMatrix - Class in dev.nm.analysis.function.matrix
This is a function that maps from R2 to a Matrix space.
R2toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R2toMatrix
 
RamerDouglasPeucker - Class in dev.nm.geometry.polyline
The Ramer-Douglas-Peucker algorithm simplifies a PolygonalChain by removing vertices which do not affect the shape of the curve to a given tolerance.
RamerDouglasPeucker(double) - Constructor for class dev.nm.geometry.polyline.RamerDouglasPeucker
Create an algorithm instance with a given threshold for the maximum distance between the original chain and a point in the simplified chain.
Rand1Bin - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
The Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vector (differential mutation); crossover by performing a uniform crossover (discrete recombination).
Rand1Bin(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin
Construct an instance of Rand1Bin.
Rand1Bin.DeRand1BinCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
This chromosome defines the Rand-1-Bin rule.
RANDOM - dev.nm.stat.descriptive.rank.Rank.TiesMethod
 
RandomBetaGenerator - Interface in dev.nm.stat.random.rng.univariate.beta
This is a random number generator that generates random deviates according to the Beta distribution.
randomCSRSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random CSRSparseMatrix.
randomDenseMatrix(int, int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random DenseMatrix.
randomDOKSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random DOKSparseMatrix.
RandomExpGenerator - Interface in dev.nm.stat.random.rng.univariate.exp
This is a random number generator that generates random deviates according to the exponential distribution.
RandomGammaGenerator - Interface in dev.nm.stat.random.rng.univariate.gamma
This is a random number generator that generates random deviates according to the Gamma distribution.
randomLILSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random LILSparseMatrix.
RandomLongGenerator - Interface in dev.nm.stat.random.rng.univariate
A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.
randomLowerTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random LowerTriangularMatrix.
RandomNumberGenerator - Interface in dev.nm.stat.random.rng.univariate
A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.
randomPositiveDefiniteMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random symmetric, positive definite matrix.
RandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random
This interface represents a univariate random process a.k.a.
RandomProcess(TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
Construct a univariate random process.
RandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.univariate.random
This interface defines a generator to construct random realizations from a univariate stochastic process.
RandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random
This class generates random realizations from a random/stochastic process.
RandomRealizationOfRandomProcess(RandomProcess, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
Construct a random realization generator from a random/stochastic process.
RandomRealizationOfRandomProcess(RandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
Construct a random realization generator from a random/stochastic process.
RandomRealizationOfRandomProcess(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
Construct a random realization generator from a discrete SDE.
RandomRealizationOfRandomProcess(SDE, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
Construct a random realization generator from an SDE.
RandomRealizationOfRandomProcess(SDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
Construct a random realization generator from an SDE.
RandomStandardNormalGenerator - Interface in dev.nm.stat.random.rng.univariate.normal
This is a random number generator that generates random deviates according to the standard Normal distribution.
randomSymmetricMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random SymmetricMatrix.
randomUpperTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a random UpperTriangularMatrix.
RandomVectorGenerator - Interface in dev.nm.stat.random.rng.multivariate
A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.
RandomWalk - Class in dev.nm.stat.stochasticprocess.univariate.random
This is the Random Walk construction of a stochastic process per SDE specification.
RandomWalk(DiscreteSDE, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
Constructs a univariate stochastic process from an SDE.
RandomWalk(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
Constructs a univariate stochastic process from an SDE.
rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
 
rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
Computes the rank by counting the number of non-zero rows in R.
rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
rank() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get the numerical rank of A as computed by the QR decomposition.
rank() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the rank of A.
rank() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Get the rank of this vector space.
rank() - Method in class dev.nm.stat.cointegration.CointegrationMLE
Get the rank of the system, i.e., the number of (real) eigenvalues.
rank(int) - Method in class dev.nm.stat.descriptive.rank.Rank
Get the rank of the i-th element.
rank(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the numerical rank of a matrix.
rank(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the numerical rank of a matrix.
Rank - Class in dev.nm.stat.descriptive.rank
Rank is a relationship between a set of items such that, for any two items, the first is either "ranked higher than", "ranked lower than" or "ranked equal to" the second.
Rank(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Rank
Compute the sample ranks of the values.
Rank(double[], double) - Constructor for class dev.nm.stat.descriptive.rank.Rank
Compute the sample ranks of the values.
Rank(double[], double, Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank
Compute the sample ranks of the values.
Rank(double[], Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank
Compute the sample ranks of the values.
Rank.TiesMethod - Enum in dev.nm.stat.descriptive.rank
The method for assigning ranks when some values are equal (called 'ties').
RankOneMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
The Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
RankOneMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.RankOneMinimizer
Construct a multivariate minimizer using the Rank One method.
ranks() - Method in class dev.nm.stat.descriptive.rank.Rank
Get the ranks of the values.
Rastrigin - Class in dev.nm.analysis.function.special
The Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.
Rastrigin(int) - Constructor for class dev.nm.analysis.function.special.Rastrigin
Constructs a Rastrigin function.
rate0 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
rate1 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
ratioTest(SimplexTable, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule
This is pivot row selection (Ratio test) rule.
ratioTest(SimplexTable, int) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting
This is pivot row selection (Ratio test) rule.
RayleighDistribution - Class in dev.nm.stat.distribution.univariate
The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and have the Rayleigh distributions.
RayleighDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.RayleighDistribution
Construct a Rayleigh distribution.
RayleighRNG - Class in dev.nm.stat.random.rng.univariate
This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.
RayleighRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.RayleighRNG
Constructs a random number generator to sample from the Rayleigh distribution.
rBar() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
Gets the average correlation \(\bar{r}\).
rbind(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of matrices by rows.
rbind(SparseMatrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse matrices by rows.
rbind(SparseVector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of sparse vectors by rows and returns a sparse matrix.
rbind(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines an array of vectors by rows.
rbind(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Combines a list of array of vectors by rows.
rbinom(int, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Generates n random binomial numbers.
rbinom(int, int, Vector, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Generates n random binomial numbers.
readCSV1d(InputStream) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array.
readCSV1d(InputStream, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array.
readCSV1d(InputStream, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array, with a given separator which overrides the default separator.
readCSV1d(String) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array.
readCSV1d(String, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array.
readCSV1d(String, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils
Read a single-column CSV file (output by write.csv from R) into a 1-D double array, with a given separator which overrides the default separator.
readCSV2d(InputStream) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array.
readCSV2d(InputStream, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array.
readCSV2d(InputStream, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array, with a given separator which overrides the default separator.
readCSV2d(String) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV file (output by write.csv from R) into a 2-D double array.
readCSV2d(String, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV file (output by write.csv from R) into a 2-D double array.
readCSV2d(String, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils
Read a multi-column CSV file (output by write.csv from R) into a 2-D double array, with a given separator which overrides the default separator.
real() - Method in class dev.nm.number.complex.Complex
Get the real part of this complex number.
Real - Class in dev.nm.number
A real number is an arbitrary precision number.
Real(double) - Constructor for class dev.nm.number.Real
Construct a Real from a double.
Real(long) - Constructor for class dev.nm.number.Real
Construct a Real from an integer.
Real(String) - Constructor for class dev.nm.number.Real
Construct a Real from a String.
Real(BigDecimal) - Constructor for class dev.nm.number.Real
Construct a Real from a BigDecimal.
Real(BigInteger) - Constructor for class dev.nm.number.Real
Construct a Real from a BigInteger.
RealInterval - Class in dev.nm.interval
This is an interval on the real line.
RealInterval(Double, Double) - Constructor for class dev.nm.interval.RealInterval
Construct an interval on the real line.
Realization - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime
This is a univariate time series indexed real numbers.
Realization.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime
This is the TimeSeries.Entry for a real number -indexed univariate time series.
realizedReturns(int) - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
Gets the last H-period accumulated realized return.
RealMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype
This is a Real matrix.
RealMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
Construct a Real matrix.
RealMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
Construct a Real matrix.
RealMatrix(Real[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
Construct a Real matrix.
RealScalarFunction - Interface in dev.nm.analysis.function.rn2r1
A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).
RealScalarFunctionChromosome - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
This chromosome encodes a real valued function.
RealScalarFunctionChromosome(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
Construct an instance of RealScalarFunctionChromosome.
RealScalarSubFunction - Class in dev.nm.analysis.function.rn2r1
This constructs a RealScalarFunction from another RealScalarFunction by restricting/fixing the values of a subset of variables.
RealScalarSubFunction(RealScalarFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
Construct a scalar sub-function.
RealVectorFunction - Interface in dev.nm.analysis.function.rn2rm
A vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).
RealVectorSpace - Class in dev.nm.algebra.linear.vector.doubles.operation
A vector space is a set of vectors that are closed under some operations.
RealVectorSpace(double, Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from an array of vectors.
RealVectorSpace(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from a matrix (a set of column vectors).
RealVectorSpace(Matrix, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from a matrix (a set of column vectors).
RealVectorSpace(Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from an array of vectors.
RealVectorSpace(List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from a list of vectors.
RealVectorSpace(List<Vector>, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace
Construct a vector space from a list of vectors.
RealVectorSubFunction - Class in dev.nm.analysis.function.rn2rm
This constructs a RealVectorFunction from another RealVectorFunction by restricting/fixing the values of a subset of variables.
RealVectorSubFunction(RealVectorFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
Construct a vector-valued sub-function.
reciprocal(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the reciprocals of values.
RecursiveGridInterpolation - Class in dev.nm.analysis.curvefit.interpolation.multivariate
This algorithm works by recursively calling lower order interpolation (hence the cost is exponential), until the given univariate algorithm can be used when the remaining dimension becomes one.
RecursiveGridInterpolation(Interpolation) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation
Constructs an n-dimensional interpolation using a given univariate interpolation algorithm.
reduce(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
Deprecated.
Not supported yet.
Reference<T> - Class in dev.nm.misc.parallel
 
Reference() - Constructor for class dev.nm.misc.parallel.Reference
 
reflect(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Apply the Householder matrix, H, to a matrix (a set of column vectors), A.
reflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Reflects (or left transform) a range of columns in the underlying matrix with a given Householder.
reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector
 
reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator
 
reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Apply the Householder matrix, H, to a column vector, x.
reflectColumns(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
 
REFLECTION - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates
 
reflectRows(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
 
reflectVectors(Vector[], int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Apply the Householder matrix, H, to an array of vectors.
relations(Interval<T>) - Method in class dev.nm.interval.Interval
Determine the interval relations between this and Y.
relativeError(double, double) - Static method in class dev.nm.number.DoubleUtils
Compute the relative error for {x1, x0}.
RelativeTolerance - Class in dev.nm.misc.algorithm.iterative.tolerance
The stopping criteria is that the norm of the residual r relative to the input base is equal to or smaller than the specified tolerance, that is, ||r||2/base ≤ tolerance
RelativeTolerance(double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
Construct an instance with RelativeTolerance.DEFAULT_TOLERANCE.
RelativeTolerance(double, double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance
Construct an instance with specified tolerance.
remainder() - Method in class dev.nm.analysis.function.polynomial.HornerScheme
Get the remainder, P(x0).
REMAINING_ROWS_PCT_THRESHOLD - Static variable in class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
remove() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator
Overridden to avoid the vector being altered.
remove(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
removeActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Remove an active index.
removeActiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Remove an active constraint by index.
removeAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
removeContext() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
 
removeEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
 
removeEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree
 
removeEdge(E) - Method in interface dev.nm.graph.Graph
Removes an edge from this graph.
removeEdge(E) - Method in class dev.nm.graph.type.SparseGraph
 
removeInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Remove an inactive index.
removeInactiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet
Remove an active constraint by index.
removeIsolatedVertices(Graph<V, E>) - Static method in class dev.nm.graph.GraphUtils
Removes those nodes that have no edges from a graph.
removeMaxEdge() - Method in class dev.nm.graph.community.GirvanNewman
Removes the edge with the highest edge-betweeness.
removeVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
removeVertex(V) - Method in interface dev.nm.graph.Graph
Removes a vertex from this graph.
removeVertex(V) - Method in class dev.nm.graph.type.SparseGraph
 
removeVertex(V) - Method in class dev.nm.graph.type.SparseTree
 
renameCol(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Renames column i.
renameRow(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
Renames row i.
rep(double, int) - Static method in class dev.nm.number.DoubleUtils
Generates an array of doubles of repeated values.
rep(int, int) - Static method in class dev.nm.number.DoubleUtils
Generates an array of ints of repeated values.
RepeatedCoordinatesException - Exception in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
 
RepeatedCoordinatesException(MatrixCoordinate) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.RepeatedCoordinatesException
 
replaceInPlace(Matrix, int, int, int, int, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Replaces a sub-matrix of a matrix with a smaller matrix.
replaceInPlace(Vector, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Replaces a sub-vector with a given smaller vector.
Resampler - Interface in dev.nm.stat.random.sampler.resampler
This is the interface of a re-sampler method.
ResamplerModel - Interface in tech.nmfin.portfoliooptimization.lai2010.fit
 
residuals - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
 
residuals() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Gets the residual analysis of this GLM regression.
residuals() - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
Gets the residual analysis.
residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
 
residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
 
residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
 
residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
 
residuals() - Method in interface dev.nm.stat.regression.linear.LinearModel
Gets the residual analysis of an OLS regression.
residuals() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression
 
residuals() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
 
residuals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the residuals, ε, the differences between sample and fitted values.
RESTRICTED_CONSTANT - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
This is trend type II: no restricted constant, no linear trend:
Result(NonlinearFit.Result, ACERConfidenceInterval, ACERReturnLevel, EmpiricalACER) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result
 
retainAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
ReturnLevel - Class in dev.nm.stat.evt.function
Given a GEV distribution of a random variable \(X\), the return level \(\eta\) is the value that is expected to be exceeded on average once every interval of time \(T\), with a probability of \(1 / T\).
ReturnLevel(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnLevel
Construct the return level function with the inverse function of a univariate extreme value distribution.
ReturnLevel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnLevel
Construct the return level function for a given univariate extreme value distribution.
ReturnPeriod - Class in dev.nm.stat.evt.function
The return period \(R\) of a level \(\eta\) for a random variable \(X\) is the mean number of trials that must be done for \(X\) to exceed \(\eta\).
ReturnPeriod(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod
Construct the return period function with the cumulative distribution function of a univariate extreme value distribution.
ReturnPeriod(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod
Construct the return period function for a given univariate extreme value distribution.
returns - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
Returns - Class in tech.nmfin.returns
Contains utility methods related to returns computation.
ReturnsCalculator - Interface in tech.nmfin.returns
This interface defines how return is computed from two values of a portfolio.
ReturnsCalculators - Enum in tech.nmfin.returns
Various ways of return calculations.
ReturnsMatrix - Class in tech.nmfin.returns
 
ReturnsMatrix(Matrix) - Constructor for class tech.nmfin.returns.ReturnsMatrix
 
ReturnsMatrix(Matrix, ReturnsCalculator) - Constructor for class tech.nmfin.returns.ReturnsMatrix
 
ReturnsMoments - Class in tech.nmfin.returns.moments
Contains the estimated moments of asset returns.
ReturnsMoments(Vector, Matrix) - Constructor for class tech.nmfin.returns.moments.ReturnsMoments
Constructs an instance.
ReturnsMoments.Estimator - Interface in tech.nmfin.returns.moments
The interface to estimate moments from returns.
ReturnsResamplerFactory - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
This is a factory interface to construct new instances of multivariate resamplers.
reverse(double...) - Static method in class dev.nm.number.DoubleUtils
Reverse a double array.
reverse(int...) - Static method in class dev.nm.number.DoubleUtils
Reverse an int array.
reverse(T[]) - Static method in class dev.nm.misc.ArrayUtils
Reverse an array in place.
reverseCopy(double...) - Static method in class dev.nm.number.DoubleUtils
Get a reversed copy of a double array.
reverseCopy(int...) - Static method in class dev.nm.number.DoubleUtils
Get a reversed copy of a int array.
ReversedWeibullDistribution - Class in dev.nm.stat.evt.evd.univariate
The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with \(\xi<0\).
ReversedWeibullDistribution() - Constructor for class dev.nm.stat.evt.evd.univariate.ReversedWeibullDistribution
Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\), shape \(\alpha=1\).
ReversedWeibullDistribution(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.ReversedWeibullDistribution
Create an instance with the given parameter values.
reverseRange(double[], int, int) - Static method in class dev.nm.number.DoubleUtils
Reverses a range of elements in an array.
rho() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets ρ as discussed in the reference.
Ridders - Class in dev.nm.analysis.differentiation
Ridders' method computes the numerical derivative of a function.
Ridders(RealScalarFunction, int[]) - Constructor for class dev.nm.analysis.differentiation.Ridders
Construct the derivative function of a vector-valued function using Ridder's method.
Ridders(RealScalarFunction, int[], double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
Construct the derivative function of a vector-valued function using Ridder's method.
Ridders(UnivariateRealFunction, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
Construct the derivative function of a univariate function using Ridder's method.
Ridders(UnivariateRealFunction, int, double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders
Construct the derivative function of a univariate function using Ridder's method.
RIDDERS - dev.nm.analysis.differentiation.univariate.Dfdx.Method
Ridders' method: try a sequence of decreasing increments to the compute derivative, and then extrapolate to zero using Neville's algorithm.
Riemann - Class in dev.nm.analysis.integration.univariate.riemann
This is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method.
Riemann() - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann
Construct an integrator.
Riemann(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann
Construct an integrator.
rightConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T
Get the one sided right confidence interval, [a, ∞)
rightConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F
Compute the one sided right confidence interval, [a, ∞)
rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Right multiplication by G, namely, A * G.
rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Right multiplication by P.
rightMultiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Right multiplication by G, namely, A * G.
rightOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T
Get the right, one-sided p-value.
rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey
Get the right, one-sided p-value.
rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Get the right, one-sided p-value.
rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Get the right, one-sided p-value.
rightOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F
Get the right, one-sided p-value.
rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Compute the one-sided p-value for the statistic greater than a critical value.
rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Compute the one-sided p-value for the statistic greater than a critical value.
rightReflect(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection
Apply the Householder matrix, H, to a matrix (a set of row vectors), A.
rightReflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Reflects (or right transform) a range of rows in the underlying matrix with a given Householder.
rightShift(double...) - Static method in class dev.nm.number.DoubleUtils
Perform a in-memory right-shift (by 1 cell} to an array.
rightShift(double[], int) - Static method in class dev.nm.number.DoubleUtils
Perform a in-memory right-shift (by k cells} to an array.
rightShift(T[]) - Static method in class dev.nm.misc.ArrayUtils
Get a right shifted array.
rightShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils
Get a right shifted (by 1 cell) copy of an array.
rightShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils
Get a right shifted (by k cells) copy of an array.
Ring<R> - Interface in dev.nm.algebra.structure
A ring is a set R equipped with two binary operations called addition and multiplication: + : R × R → R and ⋅ : R × R → R To qualify as a ring, the set and two operations, (R, +, ⋅), must satisfy the requirements known as the ring axioms.
RNGUtils - Class in dev.nm.stat.random.rng
Provides static methods that wraps random number generators to produce synchronized generators.
rnorm(int) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Generates n random standard Normals.
rnorm(int, RandomStandardNormalGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Generates n random standard Normals.
RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
RntoMatrix - Interface in dev.nm.analysis.function.matrix
This interface is a function that maps from Rn to a Matrix space.
RobustAdaptiveMetropolis - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
A variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011).
RobustAdaptiveMetropolis(RealScalarFunction, double, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of 0.5.
RobustAdaptiveMetropolis(RealScalarFunction, Matrix, double, double, Vector, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
Constructs a new instance with the given parameters.
RobustCointegration - Class in tech.nmfin.meanreversion.cointegration
This class runs the robust cointegration algorithm on a pair of prices to determine if their cointegration relationship is stable enough to trade.
RobustCointegration() - Constructor for class tech.nmfin.meanreversion.cointegration.RobustCointegration
 
Romberg - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes
Romberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation.
Romberg(IterativeIntegrator) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg
Extend an integrator using Romberg's method.
root() - Method in interface dev.nm.graph.RootedTree
Gets the root of this tree.
root() - Method in class dev.nm.graph.type.SparseTree
 
root() - Method in class dev.nm.graph.type.VertexTree
 
root() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
 
ROOT_2 - Static variable in class dev.nm.misc.Constants
\(\sqrt{2}\)
ROOT_2_PI - Static variable in class dev.nm.misc.Constants
\(\sqrt{2\pi}\)
ROOT_PI - Static variable in class dev.nm.misc.Constants
\(\sqrt{\pi}\)
RootedTree<V,​E extends Arc<V>> - Interface in dev.nm.graph
A rooted tree is a directed graph, and has a root to measure distance from the root.
rotate(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Deprecated.
Not supported yet.
rotate(V) - Method in class dev.nm.graph.type.SparseTree
This method re-pivots the tree with a new root vertex.
round(double, int) - Static method in class dev.nm.number.DoubleUtils
Round a number to the precision specified.
round(double, DoubleUtils.RoundingScheme) - Static method in class dev.nm.number.DoubleUtils
Round up or down a number to an integer.
round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers
 
round(Vector) - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.IntegerConstraint
 
round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
 
ROW_MAJOR_ORDER - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
This Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns).
rowIndices() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
 
rowMeans(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the row means.
rowMeanVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the row mean vector of a given matrix.
rows(Matrix, int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Construct a sub-matrix from the rows of a matrix.
rows(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the rows of a matrix.
rowSums(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the row sums.
rowSumVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the row sum vector of a given matrix.
RSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: sum of squared residuals, \(\sum \epsilon^2\).
run() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Run the genetic algorithm.
run() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
 
run() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA
Runs the regression.
run(double[][], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
Run the analysis with multi-period observations.
run(double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis
Run the analysis with single-period observations.
run(int) - Method in interface dev.nm.misc.parallel.LoopBody
This method contains the code inside the for-loop, as in a native for-loop like this:
run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm
 
run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock.BlockAlgorithm
Runs the matrix multiplication task.
run(T) - Method in interface dev.nm.misc.parallel.IterationBody
Execute a (parallel) task.
RungeKutta - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
The Runge-Kutta methods are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations.
RungeKutta(RungeKuttaStepper, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
Constructs a Runge-Kutta algorithm with the given integrator and the constant step size.
RungeKutta(RungeKuttaStepper, int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
Constructs a Runge-Kutta algorithm with the given integrator and the constant number of steps.
RungeKutta1 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the first-order Runge-Kutta formula, which is the same as the Euler method.
RungeKutta1() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
 
RungeKutta10 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the tenth-order Runge-Kutta formula.
RungeKutta10() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
 
RungeKutta2 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the second-order Runge-Kutta formula, which can be implemented efficiently with a three-step algorithm.
RungeKutta2() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
 
RungeKutta3 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the third-order Runge-Kutta formula.
RungeKutta3() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
 
RungeKutta4 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the fourth-order Runge-Kutta formula.
RungeKutta4() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
 
RungeKutta5 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the fifth-order Runge-Kutta formula.
RungeKutta5() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
 
RungeKutta6 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the sixth-order Runge-Kutta formula.
RungeKutta6() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
 
RungeKutta7 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the seventh-order Runge-Kutta formula.
RungeKutta7() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
 
RungeKutta8 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This is the eighth-order Runge-Kutta formula.
RungeKutta8() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
 
RungeKuttaFehlberg - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
The Runge-Kutta-Fehlberg method is a version of the classic Runge-Kutta method, which additionally uses step-size control and hence allows specification of a local truncation error bound.
RungeKuttaFehlberg(double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
Create a new instance of the Runge-Kutta-Fehlberg method for the given parameters, with the default safety factor value.
RungeKuttaFehlberg(double, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
Create a new instance of the Runge-Kutta-Fehlberg method with the given safety factor.
RungeKuttaIntegrator - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
This integrator works with a single-step stepper which estimates the solution for the next step given the solution of the current step.
RungeKuttaIntegrator(RungeKuttaStepper) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator
 
RungeKuttaStepper - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
 
RWEIBULL - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Reversed Weibull distribution.
RYDBERG_RINF - Static variable in class dev.nm.misc.PhysicalConstants
The Rydberg constant \(R_{\infty}\) in reciprocal meter (m-1).

S

s - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
This is the auxiliary helper to solve the dual problem.
s - Variable in exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded
This is the pricing column that does not have an eligible row that passes the ratio test, hence the problem is unbounded.
s - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot
the pivot column
s() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Gets the value of s.
s() - Method in class dev.nm.stat.descriptive.rank.Rank
\[ s = \sum(t_i^2 - t_i) \]
S - dev.nm.stat.descriptive.rank.Quantile.QuantileType
the definition in S
S - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
This is the auxiliary helper to solve the dual problem.
S() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Gets the original sample covariance matrix.
S() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result
Gets the sample covariance matrix S.
S() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Sample covariance matrix.
S() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the covariance (or correlation) matrix.
S() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis
Gets the tuned scaling matrix (this changes each time a new sample is drawn).
S_t_hat(int) - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
Predicts the accumulated H-period return at time t.
sample() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
Get the sample kurtosis (biased estimator).
sample() - Method in class dev.nm.stat.descriptive.moment.Skewness
Get the sample skewness (biased estimator).
SampleAutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.sample
This is the sample Auto-Correlation Function (ACF) for a univariate data set.
SampleAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
Construct the sample ACF for a time series.
SampleAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
Construct the sample ACF for a time series.
SampleAutoCovariance - Class in dev.nm.stat.timeseries.linear.univariate.sample
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.
SampleAutoCovariance(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
Construct the sample ACVF for a time series.
SampleAutoCovariance(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
Construct the sample ACVF for a time series.
SampleAutoCovariance.Type - Enum in dev.nm.stat.timeseries.linear.univariate.sample
the available auto-covariance types
SampleCovariance - Class in dev.nm.stat.descriptive.covariance
This class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.
SampleCovariance(Matrix) - Constructor for class dev.nm.stat.descriptive.covariance.SampleCovariance
Construct the covariance matrix of a matrix.
SampleCovariance(Matrix, boolean) - Constructor for class dev.nm.stat.descriptive.covariance.SampleCovariance
Construct the covariance matrix of a matrix.
SampleCovarianceEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator
 
SampleMeanEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator
 
SamplePartialAutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.sample
This is the sample partial Auto-Correlation Function (PACF) for a univariate data set.
SamplePartialAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
Construct the sample PACF for a time series.
SamplePartialAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
Construct the sample PACF for a time series.
scale() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow
Get the exponential of the coefficient.
scale() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the scalings applied to each variable.
scale() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
Gets the scalings applied to each variable.
scale() - Method in class dev.nm.stat.factor.pca.PCAbySVD
 
scale(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Scale each element in an array by a multiplier.
scaleColumn(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Scale a column: A[, j] = c * A[, j]
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
scaled(double) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
Scale this matrix, A, by a constant.
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
Multiply the elements in this by a scalar, element-by-element.
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
scaled(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Scale this vector by a constant, entry-by-entry.
scaled(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
scaled(double[], double) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
 
scaled(double[], double) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation
Scale each entry of a double array.
scaled(double[], double) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation
 
scaled(double[], double) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
scaled(MatrixAccess, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
c * A
scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
scaled(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Scales a vector, element-by-element.
scaled(Vector, Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Scales a vector, element-by-element.
scaled(Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
scaled(Real) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
scaled(Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
scaled(Real) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Scale this vector by a constant, entry-by-entry.
scaled(Real) - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
scaled(F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
scaled(F) - Method in interface dev.nm.algebra.structure.VectorSpace
× : F × V → V
scaledAlpha(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
Gets the scaled forward probabilities at time t.
scaledBeta(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
Gets the scaled backward probabilities at time t.
scaledBetas() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators
Gets the entire sequence of estimated (LARS) regression coefficients, scaled by the L2 norm of each row.
ScaledPolynomial - Class in dev.nm.analysis.function.polynomial
This constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow.
ScaledPolynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial
Construct a scaled polynomial, with 2 as the base of the scaling factor.
ScaledPolynomial(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial
Construct a scaled polynomial, with a base of the scaling factor.
scaleRow(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Scale a row: A[i, ] = c * A[i, ]
ScientificNotation - Class in dev.nm.number
Scientific notation expresses a number in this form x = a * 10b a is called the significand or mantissa, and 1 ≤ |a| < 10.
ScientificNotation(double) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of a double.
ScientificNotation(double, int) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of a number in this form: x = a * 10b.
ScientificNotation(long) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of a long.
ScientificNotation(BigDecimal) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of a number.
ScientificNotation(BigDecimal, int) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of a number in this form: x = a * 10b.
ScientificNotation(BigInteger) - Constructor for class dev.nm.number.ScientificNotation
Construct the scientific notation of an integer.
scores() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Gets the matrix of scores, computed using either Thompson's (1951) scores, or Bartlett's (1937) weighted least-squares scores.
scores() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the scores of supplied data on the principal components.
scoringRule() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis
Gets the scoring rule.
SDE - Class in dev.nm.stat.stochasticprocess.univariate.sde
This class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of the following form.
SDE(Drift, Diffusion) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.SDE
Construct a univariate diffusion type stochastic differential equation.
SDPDualProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem
A dual SDP problem, as in equation 14.4 in the reference, takes the following form.
SDPDualProblem(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem
Constructs a dual SDP problem.
SDPDualProblem.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem
This is the collection of equality constraints: \[ \sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0} \]
SDPPrimalProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem
A Primal SDP problem, as in equation 14.1 in the reference, takes the following form.
SDPPrimalProblem(SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem
Constructs a primal SDP problem.
sdPrincipalComponent(int) - Method in interface dev.nm.stat.factor.pca.PCA
Gets the standard deviation of the i-th principal component.
sdPrincipalComponents() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).
sdPrincipalComponents() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).
sdPrincipalComponents() - Method in class dev.nm.stat.factor.pca.PCAbySVD
Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix, though the calculation is actually done with the singular values of the data matrix)
SDPT3v4 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.
SDPT3v4_1a - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.
SDPT3v4_1b - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
Searches for a solution that optimizes the objective function from the default starting points.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
Search for a solution that optimizes the objective function from the given starting points.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
Searches for a solution that optimizes the objective function from the starting point given by K.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
[Previous Version] Searches for a solution that optimizes the objective function from the starting point given by K.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
Searches for a minimizer for the quadratic programming problem.
search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
Searches for a minimizer for the quadratic programming problem.
search() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
Performs a search for parameters with no id.
search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
 
search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
 
search(double, double) - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer.Solution
 
search(double, double) - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
 
search(double, double) - Method in interface dev.nm.root.univariate.UnivariateMinimizer.Solution
Search for a minimum within the interval [lower, upper].
search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
 
search(double, double, double) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
 
search(double, double, double) - Method in class dev.nm.root.univariate.GridSearchMinimizer.Solution
Search for a minimum within the interval [lower, upper].
search(double, double, double) - Method in interface dev.nm.root.univariate.UnivariateMinimizer.Solution
Search for a minimum within the interval [lower, upper].
search(int) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher
Performs a search for parameters for a given id.
search(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
Searches for a minimizer for the quadratic programming problem.
search(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
Search for a solution that minimizes the objective function from the given starting point.
search(Vector) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution
 
search(Vector...) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
 
search(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
Search for a solution that optimizes the objective function from the given starting points.
search(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer.Solution
 
search(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
Perform a Nelder-Mead search from an initial simplex.
search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
search(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
Search for a solution that minimizes the objective function from the given starting point.
search(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
 
search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
Search for a solution that optimizes the objective function from the given starting points.
search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
 
search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
search(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
Search for a solution that optimizes the objective function from the given starting points.
search(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
search(PrimalDualSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
Searches for a solution that optimizes the objective function from the given starting point.
search(PrimalDualSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
Searches for a solution that optimizes the objective function from the given starting point.
search(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
 
search(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
 
search(QPSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
Searches for a minimizer for the quadratic programming problem from the given starting points.
search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
 
search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
 
search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
 
search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
 
search(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
Search for a solution that optimizes the objective function from the given starting points.
search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
 
search(Ceta, double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer
Searches the maximal point of a given C(η) function within a given range.
search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
 
search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
 
sec(double) - Static method in class dev.nm.geometry.TrigMath
Returns the secant of an angle.
sech(double) - Static method in class dev.nm.geometry.TrigMath
Returns the hyperbolic secant of a hyperbolic angle.
seed(long...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
Seed the random number generator to produce repeatable sequences.
seed(long...) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
seed(long...) - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG
 
seed(long...) - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC
 
seed(long...) - Method in class dev.nm.stat.evt.timeseries.MARMASim
 
seed(long...) - Method in class dev.nm.stat.hmm.HMMRNG
 
seed(long...) - Method in class dev.nm.stat.markovchain.SimpleMC
 
seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator
 
seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG
Delegate to the underlying random long generator.
seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG
Delegate to the underlying random number generator.
seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG
Delegate to the underlying random vector generator.
seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.IID
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG
 
seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
Deprecated.
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.ThinRNG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer
Seed the random number/vector/scenario generator to produce repeatable experiments.
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3
 
seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
 
seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009
 
seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject
 
seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement
 
seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject
 
seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler
 
seed(long...) - Method in interface dev.nm.stat.random.Seedable
Seed the random number/vector/scenario generator to produce repeatable experiments.
seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk
 
seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim
 
seed(long...) - Method in class dev.nm.stat.test.distribution.pearson.AS159
 
seed(long...) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
 
seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim
 
seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim
 
seed(long...) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory
 
SEED - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
Seedable - Interface in dev.nm.stat.random
A seed-able experiment allow the same experiment to be repeated in exactly the same way.
select(double[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
Select the array elements which satisfy the boolean test.
select(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
Select the array elements which satisfy the boolean test.
select(GLMProblem, Matrix, int[]) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection.Step
 
select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC
 
select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue
 
SelectionByAIC - Class in dev.nm.stat.regression.linear.glm.modelselection
In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC.
SelectionByAIC() - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC
 
SelectionByZValue - Class in dev.nm.stat.regression.linear.glm.modelselection
In each step, the most significant factor is added, until all remaining factors are insignificant.
SelectionByZValue(double) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue
Creates an instance with the given significance level [0, 1].
SemiImplicitExtrapolation - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation
Semi-Implicit Extrapolation is a method of solving ordinary differential equations, that is similar to Burlisch-Stoer extrapolation.
SemiImplicitExtrapolation(double, int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
Create an instance of this algorithm with the given precision parameter and the maximum number of iterations allowed.
seq(double, double, double) - Static method in class dev.nm.number.DoubleUtils
Generates a sequence of doubles from from up to to with increments inc.
seq(double, double, int) - Static method in class dev.nm.number.DoubleUtils
Generate a sequence of n equi-spaced double values, from start to end (inclusive).
seq(int, double, double) - Static method in class dev.nm.number.DoubleUtils
Generate a sequence of double values with a given start value and a given constant increment.
seq(int, int) - Static method in class dev.nm.number.DoubleUtils
Generates a sequence of ints from from up to to with increments 1.
seq(int, int, int) - Static method in class dev.nm.number.DoubleUtils
Generates a sequence of ints from from up to to with increments inc.
Sequence - Interface in dev.nm.analysis.sequence
A sequence is an ordered list of (real) numbers.
set(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
Deprecated.
set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
This method is overridden to always throw VectorAccessException.
set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
Deprecated.
set(int, double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Change the value of an entry in this vector.
set(int, int) - Method in class dev.nm.misc.datastructure.SortableArray
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
set(int, int, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixAccess
Set the matrix entry at [i,j] to a value.
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Deprecated.
GivensMatrix is immutable
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Deprecated.
use the swap functions instead
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Deprecated.
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
Deprecated.
SubMatrixRef is immutable
set(int, int, double) - Method in class dev.nm.misc.datastructure.FlexibleTable
 
set(int, int, Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
set(int, int, Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
set(int, int, F) - Method in interface dev.nm.algebra.linear.matrix.generic.GenericMatrixAccess
Set the matrix entry at [i,j] to a value.
set(int, int, F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
set(int, DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
Replaces a sub-vector v[from : replacement.length] by a replacement starting at position from.
set(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
Associate this variation to a particular general constrained minimization problem.
set(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
Associate this variation to a particular general constrained minimization problem with only equality constraints.
set(T) - Method in class dev.nm.misc.parallel.Reference
 
set(T, int...) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
 
set(T, int...) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
Replaces the element at the specified position in this list with the specified element.
setCachingCriticalLine(boolean) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer
Sets the algorithm to compute and cache the whole critical line, so that optimal weights can be computed as quick as a linear search for turning points on the line.
setColumn(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Set the values for a column in the matrix, i.e., [*, j].
setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Set the values for a column in the matrix, i.e., [*, j].
setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Changes the matrix column values to a vector value.
setContext(long) - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG
Sets the context of this thread.
setDeltaT(double) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC
Sets the value of dt that will be used in the subsequent iterations.
setDomain(List<Vector>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution
Gives the domain values for the brute force search to try.
setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Set the current time differential.
setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
 
setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Set the current time differential.
setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
 
setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
 
setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
 
setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
Set the filtration for this function.
setInitials(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
Supply the starting points for the search.
setInitials(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
 
setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
setInitials(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
 
setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
 
setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
setInitials(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
 
setInitials(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
 
setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
No need to set initials for the dual problem.
setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
 
setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
 
setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
 
setInitials(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
Supply the starting points for the search.
setLicenseFile(File) - Static method in class dev.nm.misc.license.License
Overrides the default license file.
setLicenseKey(String) - Static method in class dev.nm.misc.license.License
Sets the license key for this invocation.
setParent(BFS.Node<V>) - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
 
setParent(V) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
Sets the parent of the node.
setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
 
setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory
Set the current generation.
setRiskAversionCoefficient(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP
Sets the risk aversion coefficient, effectively moving along the efficient frontier.
setRow(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Set the values for a row in the matrix, i.e., [i, *].
setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
Set the values for a row in the matrix, i.e., [i, *].
setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
Changes the matrix row values to a vector value.
setVisitTime(int) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
 
setXt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Set the current value of the stochastic process.
setXt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Set the current value of the stochastic process.
setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Set the current value of the Gaussian innovation.
setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
 
setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Set the current value of the Gaussian innovation.
setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
 
ShapiroWilk - Class in dev.nm.stat.test.distribution.normality
The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population.
ShapiroWilk(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.ShapiroWilk
Perform the Shapiro-Wilk test to test for the null hypothesis that a sample comes from a normally distributed population.
ShapiroWilkDistribution - Class in dev.nm.stat.test.distribution.normality
Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population.
ShapiroWilkDistribution(int) - Constructor for class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Construct a Shapiro-Wilk distribution.
shellsort(double...) - Static method in class dev.nm.number.DoubleUtils
Sort an array using Shell sort.
ShortestPath<V> - Interface in dev.nm.graph.algorithm.shortestpath
In graph theory, a shortest path algorithm finds a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized.
shouldUseParallel(int) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized
 
SHR0 - Class in dev.nm.stat.random.rng.univariate.uniform
SHR0 is a simple uniform random number generator.
SHR0() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR0
 
SHR3 - Class in dev.nm.stat.random.rng.univariate.uniform
SHR3 is a 3-shift-register generator with period 2^32-1.
SHR3() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR3
 
shutdown() - Method in class dev.nm.misc.parallel.ParallelExecutor
Shuts down the executor gracefully.
SiegelTukey - Class in dev.nm.stat.test.rank
The Siegel-Tukey test tests for differences in scale (variability) between two groups.
SiegelTukey(double[], double[]) - Constructor for class dev.nm.stat.test.rank.SiegelTukey
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
SiegelTukey(double[], double[], double) - Constructor for class dev.nm.stat.test.rank.SiegelTukey
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
SiegelTukey(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.SiegelTukey
Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.
sigma - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
sigma - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda
the standard deviation
sigma - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
 
sigma() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
 
sigma() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE
Get the diffusion matrix: \(\sigma(t, X_t, Z_t, ...)\).
sigma() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
 
sigma() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
Get the volatility.
sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get the white noise covariance matrix.
sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Get the white noise covariance matrix.
sigma() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the white noise variance.
sigma() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
Gets the diffusion volatility.
sigma(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the diffusion coefficient at a given time t and a position x.
Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
 
Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
 
sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated.
 
sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
Get the Ft adapted function the D[i,j] entry in the diffusion matrix.
sigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
sigma2() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
Gets the conditional variances of residuals over time.
sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
 
sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
 
sigma2(double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Compute the conditional variance based on the past information.
sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model
Computes the conditional variance based on the past information.
sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model
Computes the conditional variance based on the past information.
sign() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Gets the sign of the permutation matrix which is also the determinant.
significand() - Method in class dev.nm.number.ScientificNotation
Get the significand.
signum(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the signs of values.
SimilarMatrix - Class in dev.nm.algebra.linear.matrix.doubles.operation
Given a matrix A and an invertible matrix P, we construct the similar matrix B s.t., B = P-1AP
SimilarMatrix(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SimilarMatrix
Constructs the similar matrix B = P-1AP.
simObs(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Simulates a sequence of observations per the model specification.
SIMPLE - tech.nmfin.returns.ReturnsCalculators
The return is defined as the absolute return over the original value.
SimpleAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
This annealing function takes a random step in a uniform direction, where the step size depends only on the temperature.
SimpleAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction
 
SimpleAR1Fit - Class in tech.nmfin.portfoliooptimization.lai2010.fit
This class does a quick AR(1) fitting to the time series, essentially treating the returns as independent.
SimpleAR1Fit(Matrix) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
 
SimpleAR1Moments - Class in tech.nmfin.portfoliooptimization.lai2010.fit
 
SimpleAR1Moments() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments
 
SimpleArc<V> - Class in dev.nm.graph.type
A simple arc has two vertices: head and tail.
SimpleArc(V, V) - Constructor for class dev.nm.graph.type.SimpleArc
Construct a simple arc.
SimpleArc(V, V, double) - Constructor for class dev.nm.graph.type.SimpleArc
Construct a simple arc.
SimpleCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell
 
SimpleCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
A SimpleCellFactory produces SimpleCellFactory.SimpleCells.
SimpleCellFactory(double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
Construct an instance of a SimpleCellFactory.
SimpleCellFactory.SimpleCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
A SimpleCell implements the two genetic operations.
SimpleDoubleArrayOperation - Class in dev.nm.number.doublearray
This is a simple, single-threaded implementation of the array math operations.
SimpleDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.SimpleDoubleArrayOperation
 
SimpleEdge<V> - Class in dev.nm.graph.type
A simple edge has two vertices.
SimpleEdge(V, V) - Constructor for class dev.nm.graph.type.SimpleEdge
Construct a simple edge.
SimpleEdge(V, V, double) - Constructor for class dev.nm.graph.type.SimpleEdge
Construct a simple edge.
SimpleGARCHFit - Class in tech.nmfin.portfoliooptimization.lai2010.fit
This class does a quick GARCH(1,1) fitting to the time series, essentially treating the returns as independent.
SimpleGARCHFit(Matrix) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
 
SimpleGARCHMoments1 - Class in tech.nmfin.portfoliooptimization.lai2010.fit
Estimates the moments by GARCH model.
SimpleGARCHMoments1() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
 
SimpleGARCHMoments2 - Class in tech.nmfin.portfoliooptimization.lai2010.fit
Estimates the moments by GARCH model.
SimpleGARCHMoments2(GARCHResamplerFactory2) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
 
SimpleGridMinimizer - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
This minimizer is a simple global optimization method.
SimpleGridMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
SimpleGridMinimizer(RandomLongGenerator, double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
Construct a SimpleGridMinimizer to solve unconstrained minimization problems.
SimpleGridMinimizer.NewCellFactoryCtor - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
This factory constructs a new SimpleCellFactory for each minimization problem.
SimpleGridMinimizer.Solution - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
This is the solution to a minimization problem using SimpleGridMinimizer.
SimpleMatrixMathOperation - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
This is a generic, single-threaded implementation of matrix math operations.
SimpleMatrixMathOperation() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
SimpleMC - Class in dev.nm.stat.markovchain
This is a time-homogeneous Markov chain with a finite state space.
SimpleMC(Vector, Matrix) - Constructor for class dev.nm.stat.markovchain.SimpleMC
Constructs a time-homogeneous Markov chain with a finite state space.
SimpleTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
Abstract class for the common case where \(T^V_t = T^A_t\).
SimpleTemperatureFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
 
SimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime
This simple univariate time series simply wraps a double[] to form a time series.
SimpleTimeSeries(double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
Constructs an instance of SimpleTimeSeries.
SimplexCuttingPlaneMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.
SimplexCuttingPlaneMinimizer(LPSimplexSolver, SimplexCuttingPlaneMinimizer.CutterFactory) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
Construct a cutting-plane minimizer to solve an MILP problem.
SimplexCuttingPlaneMinimizer.CutterFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
This factory constructs a new Cutter for each MILP problem.
SimplexCuttingPlaneMinimizer.CutterFactory.Cutter - Interface in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
A Cutter defines how to cut a simplex table, i.e., how to relax a linear program so that the current non-integer solution is no longer feasible to the relaxation.
SimplexPivoting - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
A simplex pivoting finds a row and column to exchange to reduce the cost function.
SimplexPivoting.Pivot - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
the pivot
SimplexTable - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
This is a simplex table used to solve a linear programming problem using a simplex method.
SimplexTable(LPCanonicalProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Construct a simplex table from a canonical linear programming problem.
SimplexTable(LPCanonicalProblem1, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Construct a simplex table from a canonical linear programming problem.
SimplexTable(LPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Construct a simplex table from a general linear programming problem.
SimplexTable(LPProblem, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Construct a simplex table from a general linear programming problem.
SimplexTable(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Copy constructor.
SimplexTable.Label - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
 
SimplexTable.LabelType - Enum in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex
 
simplify(PolygonalChain) - Method in class dev.nm.geometry.polyline.RamerDouglasPeucker
Simplify the given polygonal chain.
Simpson - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes
Simpson's rule can be thought of as a special case of Romberg's method.
Simpson(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson
Construct an integrator that implements Simpson's rule.
simStates(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Simulates a sequence of states per the model specification.
SimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.unconstrained.annealing
Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy.
SimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
SimulatedAnnealingMinimizer(TemperatureFunction, AnnealingFunction, TemperedAcceptanceProbabilityFunction, int, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
Constructs a new instance.
sin(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the sine of a vector, element-by-element.
sin(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Sine of a complex number.
sinc(double) - Static method in class dev.nm.geometry.TrigMath
Returns the unnormalized sinc function of an angle.
SingularValueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
Computes all the singular values of a bidiagonal matrix.
SingularValueByDQDS(BidiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS
Computes all the singular values of a bidiagonal matrix.
sinh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Hyperbolic sine of a complex number.
size - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda
the size
size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen
Get the number of distinct eigenvalues.
size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition
 
size() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Gets the number of variables in the linear system.
size() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
size() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
 
size() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
size() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
size() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
size() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Get the length of this vector.
size() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
 
size() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
Get the number of points.
size() - Method in class dev.nm.analysis.function.tuple.PartialFunction
 
size() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
 
size() - Method in class dev.nm.interval.Intervals
Get the number of disjoint intervals.
size() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
size() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints
Get the number of constraints.
size() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints
 
size() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
 
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints
 
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints
 
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1.EqualityConstraints
 
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
Gets the number of constraints.
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
 
size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
 
size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries
 
size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter
Get T, the number of hidden states or observations.
size() - Method in class dev.nm.stat.dlm.univariate.DLMSeries
 
size() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter
Get T, the number of hidden states or observations.
size() - Method in class dev.nm.stat.regression.linear.panel.PanelData
Get the number of rows in the panel.
size() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
 
size() - Method in interface dev.nm.stat.stochasticprocess.timegrid.TimeGrid
Get the number of time points.
size() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
 
size() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the length of the history.
size() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries
 
size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
size() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries
Get the length of the time series.
size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
 
size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
 
size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
 
size(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
Get the size of the grid in the given dimension xi.
size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
 
size(int) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray
 
size(int) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection
Returns the size of the collection along the given dimension.
sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
sizeX() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Define the size of the grid along the x-axis.
sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
sizeY() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Define the size of the grid along the y-axis.
sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
Sk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl
This is the approximate inverse of the Hessian matrix.
skew() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Gets the skewness of this distribution.
skew() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
skew() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the skewness of this distribution.
skew() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.TDistribution
Gets the skewness of this distribution.
skew() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
skew() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
 
skew() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
skew() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
skew() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
skew() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated.
skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated.
Skewness - Class in dev.nm.stat.descriptive.moment
Skewness is a measure of the asymmetry of the probability distribution.
Skewness() - Constructor for class dev.nm.stat.descriptive.moment.Skewness
Construct an empty Skewness calculator.
Skewness(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Skewness
Construct a Skewness calculator, initialized with a sample.
Skewness(Skewness) - Constructor for class dev.nm.stat.descriptive.moment.Skewness
Copy constructor.
SmallestSubscriptRule - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting
Bland's smallest-subscript rule is for anti-cycling in choosing a pivot.
SmallestSubscriptRule() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule
 
SOCPConstraints - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Marker interface for SOCP constraints.
SOCPDualProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
This is the Dual Second Order Conic Programming problem.
SOCPDualProblem(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Constructs a dual SOCP problem.
SOCPDualProblem(SOCPDualProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem
Copy constructor.
SOCPDualProblem.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
 
SOCPDualProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
This is the Dual Second Order Conic Programming problem.
SOCPDualProblem1(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Constructs a dual SOCP problem.
SOCPDualProblem1(Vector, Matrix[], Vector[], Matrix, Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Constructs a dual SOCP problem.
SOCPDualProblem1(SOCPDualProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem1
Copy constructor.
SOCPDualProblem1.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
 
SOCPGeneralConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
This represents the SOCP general constraint of this form.
SOCPGeneralConstraint(Matrix, Vector, Vector, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint
Constructs a SOCP general constraint.
SOCPGeneralConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
This represents a set of SOCP general constraints of this form.
SOCPGeneralConstraints() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
Constructs a set of SOCP general constraints.
SOCPGeneralConstraints(SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
Constructs a set of SOCP general constraints.
SOCPGeneralConstraints(List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints
Constructs a set of SOCP general constraints.
SOCPGeneralProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Many convex programming problems can be represented in the following form.
SOCPGeneralProblem(Vector, SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
Construct a general Second Order Conic Programming problem.
SOCPGeneralProblem(Vector, List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
Construct a general Second Order Conic Programming problem.
SOCPGeneralProblem(SOCPGeneralProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem
Copy constructor.
SOCPGeneralProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Many convex programming problems can be represented in the following form.
SOCPGeneralProblem1(Vector, SOCPGeneralConstraint[], SOCPLinearInequality[], SOCPLinearEquality[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
Construct a general Second Order Conic Programming problem.\[ \begin{align*} \min_x \quad &; \mathbf{f^T} x \\ \textrm{s.t.} \quad &; \lVert {A_i}^T x + c_i \rVert_2 \leq b_i^T x + d_i,\quad i = 1,\dots,m \end{align*} \]
SOCPGeneralProblem1(Vector, List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
Construct a general Second Order Conic Programming problem.
SOCPGeneralProblem1(Vector, List<SOCPGeneralConstraint>, List<SOCPLinearInequality>, List<SOCPLinearEquality>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
Construct a general Second Order Conic Programming problem.
SOCPGeneralProblem1(SOCPGeneralProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem1
Copy constructor.
SOCPLinearBlackList - Class in tech.nmfin.portfoliooptimization.socp.constraints
A black list means that the positions of some assets must be zero.
SOCPLinearBlackList(int, Collection<Integer>) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
Creates a blacklist constraint.
SOCPLinearBlackList(int, Collection<Integer>, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearBlackList
Creates a blacklist constraint.
SOCPLinearEqualities - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
A list of SOCPLinearEquality's.
SOCPLinearEqualities() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEqualities
 
SOCPLinearEquality - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Linear equality for SOCP problem.
SOCPLinearEquality(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearEquality
 
SOCPLinearInequalities - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
SOCPLinearInequalities() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequalities
 
SOCPLinearInequality - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Linear inequality for SOCP problem.
SOCPLinearInequality(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPLinearInequality
 
SOCPLinearMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints
A maximum loan constraint.
SOCPLinearMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
Creates a maximum loan constraint.
SOCPLinearMaximumLoan(Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearMaximumLoan
Creates a maximum loan constraint.
SOCPLinearSectorExposure - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar
A sector exposure constraint.
SOCPLinearSectorExposure(Vector[], Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
Creates a sector exposure constraint.
SOCPLinearSectorExposure(Vector[], Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPLinearSectorExposure
Creates a sector exposure constraint.
SOCPLinearSectorNeutrality - Class in tech.nmfin.portfoliooptimization.socp.constraints
A sector neutrality means that the sum of weights for given sectors are zero.
SOCPLinearSectorNeutrality(Vector[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
Creates a sector neutrality constraint.
SOCPLinearSectorNeutrality(Vector[], double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSectorNeutrality
Creates a sector neutrality constraint.
SOCPLinearSelfFinancing - Class in tech.nmfin.portfoliooptimization.socp.constraints
A self financing constraint.
SOCPLinearSelfFinancing(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
Creates a self financing constraint.
SOCPLinearSelfFinancing(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearSelfFinancing
Creates a self financing constraint.
SOCPLinearZeroValue - Class in tech.nmfin.portfoliooptimization.socp.constraints
A zero value constraint.
SOCPLinearZeroValue(int) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
Creates a zero value constraint.
SOCPLinearZeroValue(int, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPLinearZeroValue
Creates a zero value constraint.
SOCPMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints
Transforms a maximum loan constraint into the compact SOCP form.
SOCPMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
Constructs a maximum loan constraint.
SOCPMaximumLoan(Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
Constructs a maximum loan constraint.
SOCPNoTradingList1 - Class in tech.nmfin.portfoliooptimization.socp.constraints
Transforms a black list (not to trade a new position) constraint into the compact SOCP form.
SOCPNoTradingList1(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
Constructs a black list constraint.
SOCPNoTradingList1(Vector, Matrix, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
Constructs a black list constraint.
SOCPNoTradingList1(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1
Constructs a black list constraint.
SOCPNoTradingList2 - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Transforms a black list (not to trade a new position) constraint into the compact SOCP form.
SOCPNoTradingList2(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
Constructs a black list constraint.
SOCPNoTradingList2(Vector, Matrix, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2
Constructs a black list constraint.
SOCPPortfolioConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
An SOCP constraint for portfolio optimization, e.g., market impact, is represented by a set of constraints in this form: \[ ||A^{T}x+c||_{2}\leq b^{T}x+d \] or this form: /[ A^T x = c, x \in \Re^m /] or this form: /[ A^T x \leq c, x \in \Re^m /]
SOCPPortfolioConstraint() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint
 
SOCPPortfolioConstraint.ConstraintViolationException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Exception thrown when a constraint is violated.
SOCPPortfolioConstraint.Variable - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
the variables involved in SOCPGeneralConstraints
SOCPPortfolioObjectiveFunction - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Constructs the objective function for portfolio optimization.
SOCPPortfolioObjectiveFunction(Matrix, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.
SOCPPortfolioObjectiveFunction(Matrix, double, SOCPRiskConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term.
SOCPPortfolioObjectiveFunction(Vector, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.
SOCPPortfolioObjectiveFunction(Vector, double, SOCPRiskConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction
Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term.
SOCPPortfolioProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Constructs an SOCP problem for portfolio optimization.
SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Constructs an SOCP problem for portfolio optimization.
SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem
Constructs an SOCP problem for portfolio optimization.
SOCPPortfolioProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Constructs an SOCP problem for portfolio optimization.
SOCPPortfolioProblem1(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Constructs an SOCP problem for portfolio optimization.
SOCPPortfolioProblem1(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem1
Constructs an SOCP problem for portfolio optimization.
SOCPRiskConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
 
SOCPRiskConstraint() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint
 
SOCPSectorExposure - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Transforms a sector exposure constraint into the compact SOCP form.
SOCPSectorExposure(Vector, Vector[], Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
Constructs a sector exposure constraint.
SOCPSectorExposure(Vector, Vector[], Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
Constructs a sector exposure constraint.
SOCPSectorNeutrality - Class in tech.nmfin.portfoliooptimization.socp.constraints
Transforms a sector neutral constraint into the compact SOCP form.
SOCPSectorNeutrality(Vector, Vector[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
Constructs a sector neutral constraint.
SOCPSectorNeutrality(Vector, Vector[], double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
Constructs a sector neutral constraint.
SOCPSelfFinancing - Class in tech.nmfin.portfoliooptimization.socp.constraints
Transforms a self financing constraint into the compact SOCP form.
SOCPSelfFinancing(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
Constructs a zero value constraint.
SOCPSelfFinancing(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
Constructs a zero value constraint.
SOCPZeroValue - Class in tech.nmfin.portfoliooptimization.socp.constraints
Transforms a zero value constraint into the compact SOCP form.
SOCPZeroValue(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
Constructs a zero value constraint.
SOCPZeroValue(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
Constructs a zero value constraint.
solution() - Method in interface dev.nm.misc.algorithm.bb.BBNode
the solution to the sub-problem associated with this node
solution() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution
 
Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
Solution(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
 
Solution(UnivariateRealFunction) - Constructor for class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
 
Solution(PrimalDualPathFollowingMinimizer, SDPDualProblem, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
Solves the semi-definite programming problem using the Homogeneous Self-Dual Path-Following algorithm.
Solution(SDPDualProblem, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
solve() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver
Solves the HJB equations (a nonlinear PDE system) to get the optimal stopping regions, i.e., the buying region (buy threshold) and the selling region (sell threshold).
solve(double, double, double, double, double) - Method in interface dev.nm.analysis.function.polynomial.root.QuarticRoot.QuarticSolver
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
 
solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
 
solve(double, double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
Solve Ax = b.
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR
In the ordinary least square sense, solve Ax = y
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD
In the ordinary least square sense, solve Ax = y
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
 
solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver
Solves iteratively Ax = b until the solution converges, i.e., the norm of residual (b - Ax) is less than or equal to the threshold.
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
 
solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
 
solve(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver
Get a particular solution for the linear system, Ax = b.
solve(Matrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver
Solves AX = B.
solve(TridiagonalMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
Solves a tridiagonal matrix equation.
solve(LowerTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
 
solve(LowerTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
 
solve(LowerTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
Solve Lx = b.
solve(LowerTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution
 
solve(UpperTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
 
solve(UpperTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
 
solve(UpperTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
Solve Ux = b.
solve(UpperTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution
 
solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
Return the input vector x.
solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
Return P-1x, where P is the diagonal matrix of A.
solve(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
Solve Mv = x, where M is the preconditioner matrix.
solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
Solve Mz = x using this SSOR preconditioner.
solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation
Perform the extrapolation.
solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
 
solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton
Solve an ODE using the Adams-Bashforth-Moulton method.
solve(ODE1stOrder) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolver
Solves the given ODE problem.
solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta
Solves a first order ODE.
solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg
Solve the given ODE using Runge-Kutta-Fehlberg method.
solve(ODE1stOrderWith2ndDerivative) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation
 
solve(PoissonEquation2D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference
Solve a Poisson's equation problem, with the given grid resolution parameters.
solve(WaveEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
Solve an one-dimensional wave equation, with the resolution parameters of the solution grid.
solve(WaveEquation2D, int, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
Solve a two-dimensional wave equation, with the resolution parameters of the solution grid.
solve(ConvectionDiffusionEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D
Solves a 1 dimensional convection-diffusion equation.
solve(HeatEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D
Solves the given one-dimensional heat equation.
solve(HeatEquation2D, int, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod
Solve the given two-dimensional heat equation problem, with the given numbers of points along the three axes in the grid (time, x, and y).
solve(Function<Vector, R>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer
 
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.CubicRoot
Solve \(ax^3 + bx^2 + cx + d = 0\).
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal
Solve a polynomial equation.
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.LinearRoot
Solve ax + b = 0.
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.PolyRoot
Get the roots/zeros of a polynomial.
solve(Polynomial) - Method in interface dev.nm.analysis.function.polynomial.root.PolyRootSolver
 
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
 
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRoot
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula
Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).
solve(Polynomial, double) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot
Solve \(ax^2 + bx + c = 0\).
solve(QuadraticFunction) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
Solves an unconstrained quadratic programming problem of this form.
solve(QuadraticFunction, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
Solves an unconstrained quadratic programming problem of this form.
solve(QuadraticFunction, LinearEqualityConstraints) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
Solves a quadratic programming problem subject to equality constraints.
solve(QuadraticFunction, LinearEqualityConstraints, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer
Solves a quadratic programming problem subject to equality constraints.
solve(RealScalarFunction[], Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
Searches for a root, x such that f(x) = 0.
solve(RealScalarFunction, RealVectorFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
Minimize a function subject to only inequality constraints.
solve(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
Minimize a function subject to only equality constraints.
solve(RealScalarFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
Minimize a function subject to only inequality constraints.
solve(UnivariateRealFunction) - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer
Minimize a univariate function.
solve(UnivariateRealFunction) - Method in class dev.nm.root.univariate.GridSearchMinimizer
Minimizes a univariate function.
solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.HalleyRoot
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
 
solve(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.root.univariate.BrentRoot
 
solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BisectionRoot
 
solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BrentRoot
 
solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.HalleyRoot
 
solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
 
solve(UnivariateRealFunction, double, double, double...) - Method in interface dev.nm.analysis.root.univariate.Uniroot
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
solve(UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.NewtonRoot
Searches for a root, x, in the interval [lower, upper] such that f(x) = 0.
solve(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.HalleyRoot
Search for a root, x, in the interval [lower, upper] such that f(x) = 0.
solve(RealVectorFunction) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
Solve the minimization problem to minimize F = vf' * vf.
solve(RealVectorFunction, Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot
Searches for a root, x such that f(x) = 0.
solve(RealVectorFunction, RntoMatrix) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer
Solve the minimization problem to minimize F = vf' * vf.
solve(ConstrainedOptimSubProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
 
solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer
 
solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer
 
solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer
 
solve(SOCPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer
 
solve(SOCPDualProblem1) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1
 
solve(LPCanonicalProblem1) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
 
solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
 
solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
 
solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2
 
solve(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPSimplexSolver
Solve an LP problem by a simplex algorithm on a simplex table
solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver
 
solve(LPRevisedSimplexSolver.Problem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver
 
solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer
 
solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer
 
solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer
 
solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1
 
solve(BruteForceIPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer
 
solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer
 
solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer
 
solve(BoxOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer
 
solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer
 
solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
 
solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
 
solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
 
solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
Solves a constrained optimization sub-problem that is already in the form of a ConstrainedOptimProblem.
solve(ConstrainedOptimProblem, Map<Integer, Double>) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
Solves a constrained sub-problem by specifying the fixing explicitly.
solve(MinMaxProblem<T>) - Method in class dev.nm.solver.multivariate.minmax.LeastPth
 
solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.root.univariate.GridSearchMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer
 
solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
Solve a minimization problem with a C2 objective function.
solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim
 
solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer
 
solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer
 
solve(P) - Method in interface dev.nm.solver.Optimizer
Solve an optimization problem, e.g., OptimProblem.
solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer
 
solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer
 
solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer
 
SomeIntegers(IPProblem) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers
Construct the integral constraint from an Integer Programming problem.
SORSweep - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
This is a building block for SOR and SSOR to perform the forward or backward sweep.
SORSweep(Matrix, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep
Construct an instance to perform forward or backward sweep for a linear system Ax = b.
SortableArray - Class in dev.nm.misc.datastructure
These arrays can be sorted according to the dictionary order.
SortableArray(int) - Constructor for class dev.nm.misc.datastructure.SortableArray
 
SortableArray(int...) - Constructor for class dev.nm.misc.datastructure.SortableArray
 
SortableArray(List<Integer>) - Constructor for class dev.nm.misc.datastructure.SortableArray
 
SortedOrderedPairs - Class in dev.nm.analysis.function.tuple
The ordered pairs are first sorted by abscissa, then by ordinate.
SortedOrderedPairs(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
 
SortedOrderedPairs(OrderedPairs) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs
 
sortInColumnOrder(SparseMatrix.Entry[], int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts an array of sparse matrix entries in column order (row indices in the same row can be in arbitrary order) in linear time.
sortInColumnOrder(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts a list of sparse matrix entries in column order (row indices in the same row can be in arbitrary order) in linear time.
sortInRowColumnOrder(SparseMatrix.Entry[], int, int, boolean, boolean) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts an array of sparse matrix entries in row-column order in linear time.
sortInRowColumnOrder(List<SparseMatrix.Entry>, int, int, boolean, boolean) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts a list of sparse matrix entries in row-column order in linear time.
sortInRowOrder(SparseMatrix.Entry[], int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts an array of sparse matrix entries in row order (column indices in the same row can be in arbitrary order) in linear time.
sortInRowOrder(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Sorts a list of sparse matrix entries in row order (column indices in the same row can be in arbitrary order) in linear time.
SparseDAGraph<V,​E extends Arc<V>> - Class in dev.nm.graph.type
This class implements the sparse directed acyclic graph representation.
SparseDAGraph() - Constructor for class dev.nm.graph.type.SparseDAGraph
Construct an empty directed acyclic graph.
SparseDAGraph(boolean) - Constructor for class dev.nm.graph.type.SparseDAGraph
Construct an empty directed acyclic graph.
SparseDAGraph(DAGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDAGraph
(Copy) construct a directed acyclic graph from another directed acyclic graph.
SparseDiGraph<V,​E extends Arc<V>> - Class in dev.nm.graph.type
This class implements the sparse directed graph representation.
SparseDiGraph() - Constructor for class dev.nm.graph.type.SparseDiGraph
Construct an empty graph.
SparseDiGraph(DiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDiGraph
(Copy) construct a graph from another graph.
SparseGraph<V,​E extends HyperEdge<V>> - Class in dev.nm.graph.type
This class implements the sparse graph representation.
SparseGraph() - Constructor for class dev.nm.graph.type.SparseGraph
Construct an empty graph.
SparseGraph(Graph<V, E>) - Constructor for class dev.nm.graph.type.SparseGraph
(Copy) construct a graph from another graph.
SparseMatrix - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
A sparse matrix stores only non-zero values.
SparseMatrix.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
This is a (non-zero) entry in a sparse matrix.
SparseMatrix.ValueArray - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
 
SparseMatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
These are the utility functions for SparseMatrix.
SparseStructure - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
This interface defines common operations on sparse structures such as sparse vector or sparse matrix.
SparseTree<V> - Class in dev.nm.graph.type
This class implements the sparse tree representation.
SparseTree(V) - Constructor for class dev.nm.graph.type.SparseTree
Construct a tree with a root.
SparseUnDiGraph<V,​E extends UndirectedEdge<V>> - Class in dev.nm.graph.type
This class implements the sparse undirected graph representation.
SparseUnDiGraph() - Constructor for class dev.nm.graph.type.SparseUnDiGraph
Construct an empty graph.
SparseUnDiGraph(UnDiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseUnDiGraph
(Copy) construct a graph from another graph.
SparseVector - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
A sparse vector stores only non-zero values.
SparseVector(double...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Constructs a sparse vector from a double[].
SparseVector(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Constructs a sparse vector.
SparseVector(int, int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Constructs a sparse vector.
SparseVector(int, Collection<SparseVector.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Constructs a sparse vector.
SparseVector(SparseVector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Copy constructor.
SparseVector(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
Constructs a sparse vector from a vector.
SparseVector.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
This is an entry in a SparseVector.
SparseVector.Iterator - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
This wrapper class overrides the Iterator.remove() method to throw an exception when called.
SpearmanRankCorrelation - Class in dev.nm.stat.descriptive.correlation
Spearman's rank correlation coefficient or Spearman's rho is a non-parametric measure of statistical dependence between two variables.
SpearmanRankCorrelation(double[], double[]) - Constructor for class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
Construct a Spearman rank calculator initialized with two samples.
spectralDensity(double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD
The density \(h\) of the spectral measure \(H\) on the interval (0,1).
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic
 
spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic
 
Spectrum - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
A spectrum is the set of eigenvalues of a matrix.
spread - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
spread() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
S = A - bB
SQPActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset
Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization.
SQPActiveSetMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.
SQPActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.
SQPActiveSetMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.
SQPActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This is the solution to a general minimization with only inequality constraints using the SQP Active Set algorithm.
SQPActiveSetMinimizer.VariationFactory - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This factory constructs a new instance of SQPASVariation for each SQP problem.
SQPActiveSetOnlyEqualityConstraint1Minimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This implementation is a modified version of Algorithm 15.1 in the reference to solve a general constrained optimization problem with only equality constraints.
SQPActiveSetOnlyEqualityConstraint1Minimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
SQPActiveSetOnlyEqualityConstraint1Minimizer(SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This factory constructs a new instance of SQPASEVariation for each SQP problem.
SQPActiveSetOnlyEqualityConstraint2Minimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2.
SQPActiveSetOnlyEqualityConstraint2Minimizer(double, double, int, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
SQPActiveSetOnlyEqualityConstraint2Minimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer
Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.
SQPActiveSetOnlyInequalityConstraintMinimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This implementation is a modified version of Algorithm 15.2 in the reference to solve a general constrained optimization problem with only inequality constraints.
SQPActiveSetOnlyInequalityConstraintMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.
SQPActiveSetOnlyInequalityConstraintMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.
SQPActiveSetOnlyInequalityConstraintMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer
Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.
SQPActiveSetOnlyInequalityConstraintMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This is the solution to a general minimization problem with only inequality constraints using the SQP Active Set algorithm.
SQPASEVariation - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints using Sequential Quadratic Programming.
SQPASEVariation1 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.
SQPASEVariation1() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
Construct a variation.
SQPASEVariation1(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
Construct a variation.
SQPASEVariation2 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
This implementation tries to find an exact positive definite Hessian whenever possible.
SQPASEVariation2() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
Construct a variation.
SQPASEVariation2(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
Construct a variation.
SQPASVariation - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming.
SQPASVariation1 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset
This implementation is a modified version of Algorithm 15.4 in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.
SQPASVariation1(double) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
Construct a variation.
sqrt(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the square roots of values.
sqrt(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the square roots of a vector, element-by-element.
sqrt(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Square root of a complex number.
squared(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the squares of a vector, element-by-element.
squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
Get the square Q matrix.
squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
 
squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
squareQ() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get the square Q matrix.
SSORPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
SSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed as A = D + L + Lt The SSOR preconditioning matrix is defined as M = (D + L)D-1(D + L)t or, parameterized by ω M(ω) = (1/(2 - ω))(D / ω + L)(D / ω)-1(D / ω + L)t
SSORPreconditioner(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
Construct an SSOR preconditioner with a symmetric coefficient matrix.
StandardCumulativeNormal - Interface in dev.nm.analysis.function.special.gaussian
The cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval \((-\infty, x]\).
standardDeviation() - Method in class dev.nm.stat.descriptive.moment.Variance
Get the standard deviation of the sample, which is the square root of the variance.
standardError() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
Get the standard errors of the fitted parameters.
StandardInterval - Class in dev.nm.analysis.integration.univariate.riemann.substitution
This transformation is for mapping integral region from [a, b] to [-1, 1].
StandardInterval(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
Construct a StandardInterval substitution rule.
standardized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
standard residual = residual / v1 / sqrt(RSS / (n-m))
StandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
An alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution.
StandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.StandardNormalRNG
 
standarizedInnovations() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel
Gets the standarized innovations (normalized by the conditional standard deviation at the time) of the time series.
standarizedInnovations() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit
 
standarizedInnovations() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit
 
start() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
START - dev.nm.interval.IntervalRelation
X starts Y.
START_INVERSE - dev.nm.interval.IntervalRelation
Y starts X.
state - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation
the simulated state
state - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation
the simulated state
state() - Method in class dev.nm.stat.hmm.HmmInnovation
Get the hidden state.
state() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Gets the current system configuration.
state() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
UP trend is -ve; DOWN trend is +ve.
StateEquation - Class in dev.nm.stat.dlm.univariate
This is the state equation in a controlled dynamic linear model.
StateEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Construct a time-invariant state equation without control variables.
StateEquation(double, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Construct a time-invariant state equation.
StateEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Construct a state equation without control variables.
StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Construct a state equation.
StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Construct a state equation.
StateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation
Copy constructor.
STATIONARY - dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
 
Statistic - Interface in dev.nm.stat.descriptive
A statistic (singular) is a single measure of some attribute of a sample (e.g., its arithmetic mean value).
StatisticFactory - Interface in dev.nm.stat.descriptive
A factory to construct a new Statistic.
statistics() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator
Get the test statistics of the factor analysis.
statistics() - Method in class dev.nm.stat.test.distribution.AndersonDarling
 
statistics() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples
 
statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample
 
statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples
 
statistics() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
 
statistics() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera
 
statistics() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors
 
statistics() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk
 
statistics() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest
 
statistics() - Method in class dev.nm.stat.test.HypothesisTest
Get the test statistics.
statistics() - Method in class dev.nm.stat.test.mean.OneWayANOVA
 
statistics() - Method in class dev.nm.stat.test.mean.T
 
statistics() - Method in class dev.nm.stat.test.rank.KruskalWallis
 
statistics() - Method in class dev.nm.stat.test.rank.SiegelTukey
 
statistics() - Method in class dev.nm.stat.test.rank.VanDerWaerden
 
statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
 
statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
 
statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan
 
statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity
 
statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White
 
statistics() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller
 
statistics() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce
Deprecated.
 
statistics() - Method in class dev.nm.stat.test.variance.Bartlett
 
statistics() - Method in class dev.nm.stat.test.variance.BrownForsythe
 
statistics() - Method in class dev.nm.stat.test.variance.F
 
statistics() - Method in class dev.nm.stat.test.variance.Levene
 
statisticsAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling
Gets the alternative Anderson-Darling statistic (adjusted for ties).
stderr() - Method in class dev.nm.stat.regression.linear.LMBeta
Gets the standard errors of the coefficients β^.
stderr() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the standard error of the residuals.
stderr() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Get the asymptotic standard errors of the estimators.
stderr() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
Get the asymptotic standard errors of the estimated parameters, φ and θ.
stdev() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
Gets the standard deviations of the elements.
stdev() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
stdev() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
Gets the stdev of the spread.
stdev(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
Gets the stdev of the last % portion in the spread.
stdev(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
Gets the standard deviation of the i-th element.
stdinno - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2
 
SteepestDescentImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
SteepestDescentMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction.
SteepestDescentMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
Construct a multivariate minimizer using a steepest descent method.
SteepestDescentMinimizer(LineSearch, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer
Construct a multivariate minimizer using a steepest descent method.
SteepestDescentMinimizer.SteepestDescentImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
This is an implementation of the steepest descent method.
SteepestDescentSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Steepest Descent method (SDM) solves a symmetric n-by-n linear system.
SteepestDescentSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
Construct a Steepest Descent method (SDM) solver.
SteepestDescentSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
Construct a Steepest Descent method (SDM) solver.
STEFAN_BOLTZMANN_SIGMA - Static variable in class dev.nm.misc.PhysicalConstants
The Stegan-Boltzmann constant \(\sigma\) in (W m-2 K-4).
step() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution
 
step() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound
 
step() - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod
Do the next iteration.
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
Do the next iteration.
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer1.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer1.Solution
 
step() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
Run a step in genetic algorithm: produce the next generation of chromosome pool.
step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution
 
step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl
 
step() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Performs one step of the leap-frogging algorithm.
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7
 
step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8
 
step(DerivativeFunction, Vector, double, double, double) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaStepper
 
StepFunction - Class in dev.nm.analysis.function.rn2r1.univariate
A step function (or staircase function) is a finite linear combination of indicator functions of intervals.
StepFunction(double) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Construct an empty step function.
StepFunction(OrderedPairs) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Construct a step function from a collection ordered pairs.
StepFunction(OrderedPairs, double) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction
Construct a step function from a collection ordered pairs.
steps(int) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Performs n steps of the leap-frogging algorithm.
StopCondition - Interface in dev.nm.misc.algorithm.stopcondition
Defines when an algorithm stops (the iterations).
StringUtils - Class in dev.nm.misc
Utility methods for string manipulation.
studentized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))
SturmCount - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes the Sturm count, the number of negative pivots encountered while factoring tridiagonal T - σ I = LDLT.
SturmCount(LDDecomposition, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount
Creates an instance for computing the Sturm count of a given robust representation (T - σ I = LDLT).
subA() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection
Constructs a covariates subset.
subarray(double[], int[]) - Static method in class dev.nm.number.DoubleUtils
Get a sub-array of the original array with the given indices.
subarray(int[], int[]) - Static method in class dev.nm.number.DoubleUtils
Get a sub-array of the original array with the given indices.
subDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the sub-diagonal of a matrix as a vector.
subDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the sub-diagonal of a sparse matrix as a sparse vector.
SubFunction<R> - Class in dev.nm.analysis.function
A sub-function, g, is defined over a subset of the domain of another (original) function, f.
SubFunction(Function<Vector, R>, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.SubFunction
Constructs a sub-function.
subject() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
Gets the subject of the row.
subMatrix(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
subMatrix(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
subMatrix(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
subMatrix(int, int, int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix
Extracts a sub-matrix given the bounds of row and column indices (inclusive).
subMatrix(Matrix, int[], int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the intersections of rows and columns of a matrix.
subMatrix(Matrix, int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the four corners of a matrix.
subMatrix(Matrix, List<Integer>, List<Integer>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the intersections of rows and columns of a matrix.
subMatrix(SparseMatrix, int[], int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the intersections of rows and columns of a sparse matrix.
subMatrix(SparseMatrix, int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory
Constructs a sub-matrix from the four corners of a sparse matrix.
SubMatrixBlock - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication
Sub-matrix block representation for block algorithm.
SubMatrixRef - Class in dev.nm.algebra.linear.matrix.doubles.operation
This is a 'reference' to a sub-matrix of a larger matrix without copying it.
SubMatrixRef(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
Constructs a reference to the whole matrix.
SubMatrixRef(Matrix, int[], int[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
Constructs a sub-matrix reference.
SubMatrixRef(Matrix, int, int, int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
Constructs a sub-matrix reference.
SubProblemMinimizer - Class in dev.nm.solver.multivariate.constrained
This minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem.
SubProblemMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
 
SubProblemMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
 
SubProblemMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer
 
SubProblemMinimizer.ConstrainedMinimizerFactory<U extends ConstrainedMinimizer<ConstrainedOptimProblem,​IterativeSolution<Vector>>> - Interface in dev.nm.solver.multivariate.constrained
This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.
SubProblemMinimizer.IterativeSolution<Vector> - Interface in dev.nm.solver.multivariate.constrained
 
SubstitutionRule - Interface in dev.nm.analysis.integration.univariate.riemann.substitution
A substitution rule specifies \(x(t)\) and \(\frac{\mathrm{d} x}{\mathrm{d} t}\).
subTree(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
subTree(V) - Method in interface dev.nm.graph.RootedTree
Gets a sub-tree starting from a vertex.
subTree(V) - Method in class dev.nm.graph.type.SparseTree
 
subVector(SparseVector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets a sub-vector from a sparse vector.
subVector(Vector, int[]) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).
subVector(Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets a sub-vector from a vector.
subVector(Vector, List<Integer>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).
SubVectorRef - Class in dev.nm.algebra.linear.vector.doubles
Represents a sub-vector backed by the referenced vector, without data copying.
SubVectorRef(Vector, int, int) - Constructor for class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
SuccessiveOverrelaxationSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
The Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method.
SuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver
Construct a SOR solver with the extrapolation factor ω.
sum(double[]) - Method in class dev.nm.analysis.sequence.Summation
Partial summation of the selected terms.
sum(double...) - Static method in class dev.nm.number.big.BigDecimalUtils
Sum up big numbers.
sum(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the sum of the values.
sum(double, double, double) - Method in class dev.nm.analysis.sequence.Summation
Sum up the terms from from to to with the increment inc.
sum(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the sum of the values.
sum(int, int) - Method in class dev.nm.analysis.sequence.Summation
Sum up the terms from from to to with the increment 1.
sum(int, int, int) - Method in class dev.nm.analysis.sequence.Summation
Sum up the terms from from to to with the increment inc.
sum(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the sum of all elements in the given matrix.
sum(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
Computes the sum of all vector elements.
sum(BigDecimal...) - Static method in class dev.nm.number.big.BigDecimalUtils
Sum up the BigDecimal numbers.
sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt
Get Σ(Bt)*(dt).
sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
Get Σ(Bt)*(dt).
sum_tBtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt
Get Σ(t-0.5)*(Bt)*(dt).
sum2(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath
Get the sum of squares of the values.
Summation - Class in dev.nm.analysis.sequence
Summation is the operation of adding a sequence of numbers; the result is their sum or total.
Summation(Summation.Term) - Constructor for class dev.nm.analysis.sequence.Summation
Construct a finite summation series.
Summation(Summation.Term, double) - Constructor for class dev.nm.analysis.sequence.Summation
Construct a summation series.
Summation.Term - Interface in dev.nm.analysis.sequence
Define the terms in a summation series.
SumOfPenalties - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
This penalty function sums up the costs from a set of constituent penalty functions.
SumOfPenalties(PenaltyFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
Construct a sum-of-penalties penalty function from a set of penalty functions.
SumOfPoweredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
Defines portfolio diversification as \[ D(w) = \sum_i w_i^P \]
SumOfPoweredWeights(double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
 
SumOfSquaredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
Defines portfolio diversification as \[ D(w) = \sum_i w_i^2 \]
SumOfSquaredWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
 
sumOfWeights() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
SumOfWLogW - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification
Defines portfolio diversification as \[ D(w) = \sum_i w_i \ln(w_i) \]
SumOfWLogW() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
 
sumsOfPowersOfDifferences(int, double, double...) - Static method in class dev.nm.stat.descriptive.moment.Moments
Compute the power-th moment of an array of data with respect to a mean.
sumToInfinity(double, double) - Method in class dev.nm.analysis.sequence.Summation
Sum up the terms from from to infinity with increment inc until the series converges.
sumToInfinity(int) - Method in class dev.nm.analysis.sequence.Summation
Sum up the terms from from to infinity with increment 1 until the series converges.
sumUpLastRows(Matrix, int, int) - Static method in class tech.nmfin.signal.infantino2010.Infantino2010PCA
Sums up, for each column, the last nRows rows.
superDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the super-diagonal of a matrix as a vector.
superDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory
Gets the super-diagonal of a sparse matrix as a sparse vector.
supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule
 
supportsInterval(double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule
Return whether the given interval (a,b) is supported by this rule.
supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule
 
supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule
 
supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule
 
svd() - Method in class dev.nm.stat.factor.pca.PCAbySVD
Gets the Singular Value Decomposition (SVD) of matrix X.
SVD - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.
SVD(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
Runs the SVD decomposition on a matrix.
SVD(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
Runs the SVD decomposition on a matrix.
SVD(Matrix, boolean, double, SVD.Method) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
Runs the SVD decomposition on a matrix.
SVD.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.svd
 
SVDbyMR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3
Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
SVDbyMR3(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
Creates a singular value decomposition for a matrix A.
SVDDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.svd
SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.
SVEC - Class in dev.nm.algebra.linear.matrix.doubles.operation
SVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.
SVEC(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SVEC
Construct the SVEC of a matrix.
svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution
 
svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution
Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".
svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution
 
swap(int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
Perform a Jordan Exchange to swap row r with column s.
swap(MatrixTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
Constructs a new table by exchanging the r-th row with the s-th column in A using Jordan Exchange.
swap(FlexibleTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
Constructs a new table by exchanging the r-th row with the s-th column in A using Jordan Exchange.
swapColumn(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Swaps two columns of a permutation matrix.
swapColumn(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Swap columns:
swapInPlace(FlexibleTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange
 
swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
Swaps two rows of a permutation matrix.
swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Swap rows:
symbol1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
symbol1() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
 
symbol2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
 
symbol2() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
 
SYMMETRIC_WINDOW - dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
Use a symmetric window of past and future values, centered around lag 0.
SymmetricEigenByMR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR).
SymmetricEigenByMR3(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
Creates an instance for computing the eigen decomposition for a given symmetric matrix A.
SymmetricEigenByMR3(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3
Creates an instance for computing the eigen decomposition for a given symmetric matrix A.
SymmetricEigenFor2x2Matrix - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Computes the eigen decomposition of a 2-by-2 symmetric matrix in the following form by symmetric QR algorithm.
SymmetricEigenFor2x2Matrix(double, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix
 
SymmetricKronecker - Class in dev.nm.algebra.linear.matrix.doubles.operation
Compute the symmetric Kronecker product of two matrices.
SymmetricKronecker(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SymmetricKronecker
Compute the symmetric Kronecker product of two matrices.
SymmetricMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
A symmetric matrix is a square matrix such that its transpose equals to itself, i.e., A[i][j] = A[j][i]
SymmetricMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
Construct a symmetric matrix from a 2D double[][] array.
SymmetricMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
Construct a symmetric matrix of dimension dim * dim.
SymmetricMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
Cast an (almost) symmetric matrix into SymmetricMatrix by averaging A(i,j) and A(j,i).
SymmetricMatrix(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
Cast an (almost) symmetric matrix into SymmetricMatrix.
SymmetricMatrix(SymmetricMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
Copy constructor.
SymmetricQRAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
The symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix.
SymmetricQRAlgorithm(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Runs the QR algorithm on a symmetric matrix.
SymmetricQRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Runs the QR algorithm on a symmetric matrix.
SymmetricQRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm
Runs the QR algorithm on a symmetric matrix.
SymmetricSuccessiveOverrelaxationSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
The Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in each iteration one forward sweep followed by one backward sweep.
SymmetricSuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver
Construct a SSOR solver with the extrapolation factor ω.
SymmetricSVD - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd
This algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm.
SymmetricSVD(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Calculates the SVD of A.
SymmetricSVD(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Calculates the SVD of A.
SymmetricTridiagonalDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
Given a square, symmetric matrix A, we find Q such that Q' * A * Q = T , where T is a tridiagonal matrix.
SymmetricTridiagonalDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
Runs the tridiagonal decomposition for a square, symmetric matrix.
SYMMETRY - dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
For a symmetric matrix.
SYNC_RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
SYNC_UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
synchronizedRLG(RandomLongGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
Returns a synchronized (thread-safe) RandomLongGenerator backed by a specified generator.
synchronizedRNG(RandomNumberGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
Returns a synchronized (thread-safe) RandomNumberGenerator backed by a specified generator.
synchronizedRVG(RandomVectorGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils
Returns a synchronized (thread-safe) RandomVectorGenerator backed by a specified generator.
SynchronizedStatistic - Class in dev.nm.stat.descriptive
This is a thread-safe wrapper of Statistic by synchronizing all public methods so that only one thread at a time can access the instance.

T

t - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
distinct population eigenvalues
t() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
t() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
Get the transpose of this matrix.
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
The transpose of a diagonal matrix is the same as itself.
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
t(A)
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
The transpose of a symmetric matrix is the same as itself.
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
The transpose of a permutation matric is the same as its inverse.
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
t() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem
Gets the penalization parameter t for L1 regularization.
t() - Method in class dev.nm.stat.descriptive.rank.Rank
/[ t = \sum(t_i^3 - t_i) \]
t() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem
Get the penalization parameter for the constrained form of LASSO.
t() - Method in class dev.nm.stat.regression.linear.LMBeta
Gets the t- or z- value of the regression coefficients β^.
t() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
Get the current time.
t() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
Get the current time.
t(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Gets the value on the time axis at index k.
t(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the value on the time-axis at index k.
T - Class in dev.nm.stat.test.mean
Student's t-test tests for the equality of means, for the one-sample case, against a hypothetical mean, and for two-sample case, of two populations.
T - Variable in class tech.nmfin.signal.infantino2010.Infantino2010PCA
 
T(double[], double) - Constructor for class dev.nm.stat.test.mean.T
Construct a one-sample location test of whether the mean of a normally distributed population has a value specified in a null hypothesis.
T(double[], double[]) - Constructor for class dev.nm.stat.test.mean.T
Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.
T(double[], double[], boolean, double) - Constructor for class dev.nm.stat.test.mean.T
Construct a two sample location test of the null hypothesis that the means of two normally distributed populations are equal.
T(double[], double[], double) - Constructor for class dev.nm.stat.test.mean.T
Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition
Gets the symmetric tridiagonal T matrix.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
Gets T, such that T = Q * A * Q.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm
 
T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Get the transformation matrix, T, such that T * A = U.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
Get the transformation matrix, T, such that T * A = U.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the transformation matrix, T, such that T * A = U.
T() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
Get the transformed matrix T.
T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Gets the time period of interest, that is, the range of t, (0 < t < T).
T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Get the time period of interest, that is, the range of t, (0 < t < T).
T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D
Gets the time period of interest, that is, the range of t, (0 < t < T).
T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D
Gets the time period of interest, that is, the range of t, (0 < t < T).
T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D
Gets the time period of interest, that is, the range of t, (0 < t < T).
T() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid
Get the end time.
T() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
 
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
Get the lower limit of the integral.
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
 
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
 
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
 
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
 
ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
 
ta() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
Get the lower limit of the integral.
table - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
 
Table - Interface in dev.nm.misc.datastructure
A table is a means of arranging data in rows and columns.
tail() - Method in interface dev.nm.graph.Arc
Get the tail of this arc.
tail() - Method in class dev.nm.graph.type.SimpleArc
 
tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt
 
tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR
 
tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR
 
tallR() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition
Get the tall R matrix.
tan(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Tangent of a complex number.
tangentAt(SortedOrderedPairs, int) - Method in interface dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangent
Compute the tangent at the given index k, from the given collection of points.
tanh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction
Hyperbolic tangent of a complex number.
tau - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
population eigenvalues in ascending order
tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result
Gets estimated population eigenvalues in ascending order.
TauEstimator - Class in dev.nm.stat.covariance.nlshrink
Non-linear shrinkage estimator of population eigenvalues.
TauEstimator(int, int, Vector) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator
 
TauEstimator(int, int, Vector, int, double) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator
 
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
Get the upper limit of the integral.
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
 
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
 
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
 
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
 
tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
 
tb() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
Get the upper limit of the integral.
TDistribution - Class in dev.nm.stat.distribution.univariate
The Student t distribution is the probability distribution of t, where \[ t = \frac{\bar{x} - \mu}{s / \sqrt N} \] \(\bar{x}\) is the sample mean; μ is the population mean; s is the square root of the sample variance; N is the sample size; The importance of the Student's distribution is when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.
TDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.TDistribution
Construct a Student's t distribution.
tech.nmfin.meanreversion.cointegration - package tech.nmfin.meanreversion.cointegration
 
tech.nmfin.meanreversion.cointegration.check - package tech.nmfin.meanreversion.cointegration.check
 
tech.nmfin.meanreversion.daspremont2008 - package tech.nmfin.meanreversion.daspremont2008
 
tech.nmfin.meanreversion.elliott2005 - package tech.nmfin.meanreversion.elliott2005
 
tech.nmfin.meanreversion.hvolatility - package tech.nmfin.meanreversion.hvolatility
 
tech.nmfin.meanreversion.volarb - package tech.nmfin.meanreversion.volarb
 
tech.nmfin.portfoliooptimization - package tech.nmfin.portfoliooptimization
 
tech.nmfin.portfoliooptimization.clm - package tech.nmfin.portfoliooptimization.clm
 
tech.nmfin.portfoliooptimization.corvalan2005 - package tech.nmfin.portfoliooptimization.corvalan2005
 
tech.nmfin.portfoliooptimization.corvalan2005.constraint - package tech.nmfin.portfoliooptimization.corvalan2005.constraint
 
tech.nmfin.portfoliooptimization.corvalan2005.diversification - package tech.nmfin.portfoliooptimization.corvalan2005.diversification
 
tech.nmfin.portfoliooptimization.lai2010 - package tech.nmfin.portfoliooptimization.lai2010
 
tech.nmfin.portfoliooptimization.lai2010.ceta - package tech.nmfin.portfoliooptimization.lai2010.ceta
 
tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer - package tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer
 
tech.nmfin.portfoliooptimization.lai2010.ceta.npeb - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb
 
tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
 
tech.nmfin.portfoliooptimization.lai2010.fit - package tech.nmfin.portfoliooptimization.lai2010.fit
 
tech.nmfin.portfoliooptimization.lai2010.optimizer - package tech.nmfin.portfoliooptimization.lai2010.optimizer
 
tech.nmfin.portfoliooptimization.markowitz - package tech.nmfin.portfoliooptimization.markowitz
 
tech.nmfin.portfoliooptimization.markowitz.constraints - package tech.nmfin.portfoliooptimization.markowitz.constraints
 
tech.nmfin.portfoliooptimization.nmsaam - package tech.nmfin.portfoliooptimization.nmsaam
 
tech.nmfin.portfoliooptimization.socp.constraints - package tech.nmfin.portfoliooptimization.socp.constraints
 
tech.nmfin.portfoliooptimization.socp.constraints.ybar - package tech.nmfin.portfoliooptimization.socp.constraints.ybar
 
tech.nmfin.returns - package tech.nmfin.returns
 
tech.nmfin.returns.moments - package tech.nmfin.returns.moments
 
tech.nmfin.signal.infantino2010 - package tech.nmfin.signal.infantino2010
 
tech.nmfin.trend.dai2011 - package tech.nmfin.trend.dai2011
 
tech.nmfin.trend.kst1995 - package tech.nmfin.trend.kst1995
 
temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction
 
temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction
Matlab's default: @temperatureexp (default) - T = T0 * 0.95^k.
temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction
Matlab: @temperaturefast - The temperature is equal to InitialTemperature / k.
temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
Gets the temperature at time t.
TemperatureFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction
A temperature function defines a temperature schedule used in simulated annealing.
TemperedAcceptanceProbabilityFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
A tempered acceptance probability function computes the probability that the next state transition will be accepted.
test(double) - Method in interface dev.nm.number.DoubleUtils.ifelse
Decide whether x satisfies the boolean test.
theta - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda
the scale parameter
theta() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess
 
theta() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess
Get the mean reversion rate.
theta() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Get all the MA coefficients.
theta() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get all the MA coefficients.
theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
theta(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
The canonical parameter of the distribution in terms of the mean μ.
theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
Get the coefficients of the linear predictor.
theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
Get the coefficients of the linear predictor.
theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm
Get the coefficients of the linear predictor.
theta(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
Gets the coefficients of the linear predictor.
thetaPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
Get the polynomial (1 + θ).
ThinRNG - Class in dev.nm.stat.random.rng.univariate
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
ThinRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.univariate.ThinRNG
Constructs a thinned RNG.
ThinRVG - Class in dev.nm.stat.random.rng.multivariate
Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.
ThinRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.ThinRVG
Constructs a thinned RVG.
ThomasAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem
Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.
ThomasAlgorithm() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm
 
THOMSON - dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
Thomson's (1951) scores
ThreadIDRLG - Class in dev.nm.stat.random.rng.concurrent.context
This uniform number generator generates independent sequences of random numbers per thread, hence thread-safe.
ThreadIDRLG() - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
Constructs a per-context repeatable RLG.
ThreadIDRLG(int) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
Constructs a per-context repeatable RLG.
ThreadIDRLG(int, long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
Constructs a per-context repeatable RLG.
ThreadIDRLG(long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG
Constructs a per-context repeatable RLG.
ThreadIDRNG - Class in dev.nm.stat.random.rng.concurrent.context
This random number generator generates independent sequences of random numbers per thread, hence thread-safe.
ThreadIDRNG(int, long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG
Constructs a per-context repeatable RNG.
threshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM
Gets a suggested trading threshold based on an OU process.
throwIfDifferentDimension(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Throws if A1.nRows() != A2.nRows() Or A1.nCols() != A2.nCols()
throwIfIncompatible4Multiplication(Table, Vector) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Throws if A.nCols() != v.size()
throwIfIncompatible4Multiplication(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Throws if A1.nCols() != A2.nRows()
throwIfInvalidColumn(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Throws if accessing an out of range column.
throwIfInvalidIndex(Vector, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if an index is a valid index.
throwIfInvalidRow(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck
Throws if accessing an out of range row.
throwIfNotEqualSize(Vector, Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils
Checks if the input vectors have the same size.
throwIfNotNull(RuntimeException) - Static method in class dev.nm.misc.ExceptionUtils
This is a wrapper method that throws a RuntimeException if it is not null.
Ties<T> - Class in dev.nm.combinatorics
Count the number of occurrences of each distinctive value.
Ties(List<T>) - Constructor for class dev.nm.combinatorics.Ties
Count the number of occurrences of each distinctive value.
time() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
Gets the time of the row.
time() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Get the current time.
time() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
Get the current time.
time() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
Gets the forecast time.
time(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the i-th time point.
time(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Get the i-th timestamp.
time(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Get the i-th time.
TimeGrid - Interface in dev.nm.stat.stochasticprocess.timegrid
Specify the time points in a grid or axis.
TimeInterval - Class in dev.nm.misc.datastructure.time
This is a time interval.
TimeInterval(LocalDateTime, LocalDateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeInterval
Construct a time interval from two time points.
TimeIntervals - Class in dev.nm.misc.datastructure.time
This is a collection of time intervals TimeInterval.
TimeIntervals() - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
Construct an empty collection of time interval.
TimeIntervals(Interval<LocalDateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
Construct a collection consisting of one time interval.
TimeIntervals(Interval<LocalDateTime>...) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
Construct a collection of time intervals.
TimeIntervals(Intervals<LocalDateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
Copy constructor.
TimeIntervals(LocalDateTime, LocalDateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals
Construct a collection consisting of one time interval.
times() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration
Get the entire time grid.
times() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
 
TimeSeries<T extends Comparable<? super T>,​V,​E extends TimeSeries.Entry<T,​V>> - Interface in dev.nm.stat.timeseries.datastructure
A time series is a serially indexed collection of items.
TimeSeries.Entry<T,​V> - Interface in dev.nm.stat.timeseries.datastructure
A time series is composed of a sequence of Entrys.
timestamps() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Get all the timestamps.
timestamps() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
Get all the timestamps.
to1DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get all matrix entries in the form of an 1D double[].
to2DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get all matrix entries in the form of a 2D double[][] array.
toArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
toArray() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef
 
toArray() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
toArray() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
toArray() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
toArray() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Cast this vector into a 1D double[].
toArray() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
toArray() - Method in class dev.nm.misc.datastructure.MathTable.Row
Converts the row to a double[], excluding the index.
toArray() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
Get the sorted sample.
toArray() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
Convert this multivariate time series into an array of vectors.
toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
 
toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries
 
toArray() - Method in interface dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries
Convert this time series into an array, discarding the timestamps.
toArray(T[]) - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
toBigDecimal() - Method in class dev.nm.number.Real
Convert this number to a BigDecimal.
toColumns(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get an array of all column vectors from a matrix.
toContext(HouseholderInPlace.Householder) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder
 
toCSV() - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
toDense() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.Densifiable
Densify a matrix, i.e., convert a matrix implementation to the standard dense matrix, DenseMatrix.
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
toDouble() - Method in class dev.nm.number.complex.Complex
Cast the complex number to a Double if it is a real number.
toEntryArray(int[], int[], double[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
 
toEntryList(int[], int[], double[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
 
toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralLessThanConstraints
 
toGreaterThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.LessThanConstraints
Convert the less-than or equal-to constraints to greater-than or equal-to constraints.
toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
 
toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints
 
Tolerance - Interface in dev.nm.misc.algorithm.iterative.tolerance
The tolerance criteria for an iterative algorithm to stop.
toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints
 
toLessThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.GreaterThanConstraints
Convert the greater-than or equal-to constraints to less-than or equal-to constraints.
toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
 
toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints
 
toMatrix() - Method in class dev.nm.misc.datastructure.FlexibleTable
Gets a copy of the flexible table in the form of a matrix.
toMatrix() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
toMatrix() - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries
Convert this multivariate time series into an m x n matrix, where m is the dimension, and n the length.
toMatrix() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
toMatrix(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
Cast a time series into a column matrix, discarding the timestamps.
TopNOptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization
 
TopNOptimizationAlgorithm(PortfolioOptimizationAlgorithm, int, double) - Constructor for class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm
 
topologicalOrder(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
 
topologicalOrder(V) - Method in interface dev.nm.graph.DAGraph
Get the topological order of a vertex.
topologicalOrder(V) - Method in class dev.nm.graph.type.SparseDAGraph
 
topologicalOrder(V) - Method in class dev.nm.graph.type.SparseTree
 
toPrimitive(Double[]) - Static method in class dev.nm.number.DoubleUtils
Convert a Double array to a primitive double array.
toRows(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get an array of all row vectors from a matrix.
toStop(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration.StoppingCriterion
Check whether we stop with the current eigenvector.
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
 
toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
toString() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
toString() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
toString() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef
 
toString() - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
toString() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
 
toString() - Method in class dev.nm.analysis.function.tuple.Triple
 
toString() - Method in class dev.nm.graph.algorithm.traversal.BFS.Node
 
toString() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node
 
toString() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
 
toString() - Method in class dev.nm.graph.community.EdgeBetweeness
 
toString() - Method in class dev.nm.graph.community.GirvanNewman
 
toString() - Method in class dev.nm.graph.type.SimpleArc
 
toString() - Method in class dev.nm.graph.type.SimpleEdge
 
toString() - Method in class dev.nm.graph.type.SparseDiGraph
 
toString() - Method in class dev.nm.graph.type.SparseGraph
 
toString() - Method in class dev.nm.graph.type.VertexTree
 
toString() - Method in class dev.nm.interval.Interval
 
toString() - Method in class dev.nm.interval.Intervals
 
toString() - Method in class dev.nm.misc.algorithm.ActiveSet
 
toString() - Method in class dev.nm.misc.datastructure.FlexibleTable
 
toString() - Method in class dev.nm.misc.datastructure.IdentityHashSet
 
toString() - Method in class dev.nm.misc.datastructure.SortableArray
 
toString() - Method in class dev.nm.misc.datastructure.time.LocalDateInterval
 
toString() - Method in class dev.nm.misc.datastructure.time.LocalDateTimeInterval
 
toString() - Method in class dev.nm.number.complex.Complex
 
toString() - Method in class dev.nm.number.Real
 
toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
 
toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints
 
toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
 
toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1
 
toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
 
toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer
 
toString() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
toString() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
 
toString() - Method in class dev.nm.stat.descriptive.covariance.Covariance
 
toString() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
 
toString() - Method in class dev.nm.stat.descriptive.moment.Mean
 
toString() - Method in class dev.nm.stat.descriptive.moment.Moments
 
toString() - Method in class dev.nm.stat.descriptive.moment.Skewness
 
toString() - Method in class dev.nm.stat.descriptive.moment.Variance
 
toString() - Method in class dev.nm.stat.descriptive.rank.Max
 
toString() - Method in class dev.nm.stat.descriptive.rank.Min
 
toString() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster
 
toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval
 
toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
 
toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
 
toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData
 
toString() - Method in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix
 
toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
 
toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
 
toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
 
toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
 
toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
 
toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel
 
toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
 
toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
 
toString() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
 
toString() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
toString() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
 
toString() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
 
toString() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
 
toString() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM
 
toString() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995
 
toString(double...) - Static method in class dev.nm.number.DoubleUtils
Print out numbers to a string.
toString(double[][]) - Static method in class dev.nm.number.DoubleUtils
Print out a 2D array, double[][] to a string.
toString(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
Get the String representation of a matrix.
toString(SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils
Returns a string representation of a SparseMatrix.
toVector(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils
Cast a time series into a vector, discarding the timestamps.
tr(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure
Compute the sum of the diagonal elements, i.e., the trace of a matrix.
TRACE - dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
the TRACE test
track(V, int) - Method in class dev.nm.graph.algorithm.traversal.BFS
 
track(V, int) - Method in class dev.nm.graph.algorithm.traversal.DFS
 
track(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
Runs the traversal algorithm on a graph from a designated root.
trades() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
 
TradingPair - Class in tech.nmfin.meanreversion.cointegration
 
TradingPair(String, String, Vector, Vector, double) - Constructor for class tech.nmfin.meanreversion.cointegration.TradingPair
Constructs a related pair for trading, e.g., cointegrated pair.
train(int[], DiscreteHMM) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
Constructs a trained (discrete) hidden Markov model, one iteration.
train(MixtureHMM, double[]) - Static method in class dev.nm.stat.hmm.mixture.MixtureHMMEM
Constructs a trained mixture hidden Markov model, one iteration.
transform(double[]) - Method in interface dev.nm.dsp.univariate.operation.system.doubles.Filter
Transforms the input signal into the output signal.
transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage
 
transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension
 
transform(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer
 
transform(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer1
 
transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation
 
transpose(MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation
Get the transpose of A.
transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation
 
transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
Return the input vector x.
transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner
Pt = P-1 for Jacobi preconditioner.
transposeSolve(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner
Solve Mtv = x, where M is the preconditioner matrix.
transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner
Mtx = M-1x as M is symmetric.
Trapezoidal - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes
The Trapezoidal rule is a closed type Newton-Cotes formula, where the integral interval is evenly divided into N sub-intervals.
Trapezoidal(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Trapezoidal
Construct an integrator that implements the Trapezoidal rule.
TraversalFromRoots<V> - Class in dev.nm.graph.algorithm.traversal
A graph traversal is the problem of visiting all the nodes in a graph in a particular manner.
TraversalFromRoots(Graph<? extends V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.TraversalFromRoots
Constructs a traversal order of a graph.
traverse(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots
Runs the traversal algorithm on a graph from a designated root.
Tree<V,​E extends HyperEdge<V>> - Interface in dev.nm.graph
A tree is an undirected graph in which any two vertices are connected by exactly one simple path.
trees() - Method in interface dev.nm.graph.Forest
Get the disjoint set of trees.
trend() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
TrendType - Enum in dev.nm.stat.test.timeseries.adf
These are the three versions of the Augmented Dickey-Fuller (ADF) test.
TriangularDistribution - Class in dev.nm.stat.distribution.univariate
The triangular distribution is a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b.
TriangularDistribution(double, double, double) - Constructor for class dev.nm.stat.distribution.univariate.TriangularDistribution
Constructs an instance of a Triangular distribution.
TridiagonalDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
This class locates deflation in a tridiagonal matrix.
TridiagonalDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
 
TridiagonalDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch
 
TriDiagonalization - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
A tri-diagonal matrix A is a matrix such that it has non-zero elements only in the main diagonal, the first diagonal below, and the first diagonal above.
TriDiagonalization(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization
Runs the tri-diagonalization process for a symmetric matrix.
TridiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.
TridiagonalMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
Constructs a tri-diagonal matrix from a 3-row 2D double[][] array such that: the first row is the super diagonal with (dim - 1) entries; the second row is the main diagonal with dim entries; the third row is the sub diagonal with (dim - 1) entries. For example,
TridiagonalMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
Constructs a 0 tri-diagonal matrix of dimension dim * dim.
TridiagonalMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
Casts a matrix to tridiagonal by copying the 3 diagonals (ignoring all other entries).
TridiagonalMatrix(TridiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
Copy constructor performing a deep copy.
Trigamma - Class in dev.nm.analysis.function.special.gamma
The trigamma function is defined as the logarithmic derivative of the digamma function.
Trigamma() - Constructor for class dev.nm.analysis.function.special.gamma.Trigamma
 
TrigMath - Class in dev.nm.geometry
A collection of trigonometric functions complementary to those in Java's Math class.
TRIMMED_MEANS - dev.nm.stat.test.variance.Levene.Type
compute the absolute deviations from the group trimmed means
Triple - Class in dev.nm.analysis.function.tuple
A triple is a tuple of length three.
Triple(double, double, double) - Constructor for class dev.nm.analysis.function.tuple.Triple
Creates a new triple with the given values.
TrivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1
A trivariate real function takes three real arguments and outputs one real value.
TruncatedNormalDistribution - Class in dev.nm.stat.distribution.univariate
The truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both).
TruncatedNormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
Construct a truncated standard Normal distribution.
TruncatedNormalDistribution(double, double, double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
Construct a truncated Normal distribution.
TSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the diagnostic measure: total sum of squares, \(\sum (y-y_mean)^2 \).
TurningPoint - Class in tech.nmfin.portfoliooptimization.clm
Represents a turning point on a Markowitz critical line.
Twiddle<T> - Class in dev.nm.combinatorics
Generates all combinations of M elements drawn without replacement from a set of N elements.
Twiddle(Collection<T>, int) - Constructor for class dev.nm.combinatorics.Twiddle
 
TWO_SAMPLE - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
the two-sample Kolmogorov-Smirnov test
TWO_SIDED - dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
compute Dn
type - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label
the label type
TYPE_I - dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
default: the denominator is the time series length
TYPE_II - dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
the denominator is the time series length minus the lag

U

u - Variable in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D
the solution matrix
u() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial
Get u as in (x2 + ux + v).
u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Gets the value of the grid point at (xk, yj).
u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Gets the value of the grid point at (tm, xn).
u(int, int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the value of the grid point at (tk, xi, yj).
U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
Gets U, where U' = Uk * ...
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Gets U, where U' = Uk * ...
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination
Get the upper triangular matrix, U, such that T * A = U and P * A = L * U.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
Get the reduced row echelon form matrix, U, such that T * A = U.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
 
U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
Get the U matrix as in SVD decomposition.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Returns the matrix U as in A=UDV'.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle
 
U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU
 
U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition
Get the upper triangular matrix U as in the LU decomposition.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel
Get the upper triangular matrix U, such that T * A = U.
U() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets the accumulated Householder reflections applied to A.
unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Compute the multivariate ARMA unconditional mean.
unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel
Compute the multivariate ARMA unconditional mean.
unconstrainedFactory - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory
a factory that defines the unconstrained Differential Evolution operators
UnconstrainedLASSObyCoordinateDescent - Class in dev.nm.stat.regression.linear.lasso
This class solves the unconstrained form of LASSO, that is, \[ \min_w \left \{ \left \| Xw - y \right \|_2^2 + \lambda * \left \| w \right \|_1 \right \} \] by Coordinate Descent method.
UnconstrainedLASSObyCoordinateDescent(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
Solves an unconstrained LASSO problem by Coordinate Descent method.
UnconstrainedLASSObyQP - Class in dev.nm.stat.regression.linear.lasso
This class solves the unconstrained form of LASSO (i.e.
UnconstrainedLASSObyQP(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
Solves an unconstrained LASSO problem by transforming it into a single quadratic programming problem.
UnconstrainedLASSOProblem - Class in dev.nm.stat.regression.linear.lasso
A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.
UnconstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
Constructs a LASSO problem.
UnconstrainedLASSOProblem(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem
Copy constructor.
UNDEFINED - dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
an undefined label
UNDEFINED - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
 
unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V) - Static method in class dev.nm.graph.GraphUtils
Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.
unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V, GraphUtils.EdgeFactory<V, N, E, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils
Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.
UnDiGraph<V,​E extends UndirectedEdge<V>> - Interface in dev.nm.graph
An undirected graph is a graph, or set of nodes connected by edges, where an edge does not differentiate between (a, b) or (b, a).
UndirectedEdge<V> - Interface in dev.nm.graph
A tagging interface for implementations of an undirected graph that accept only undirected edges.
uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm
This is a uniform random number generator.
uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory
the uniform random number generator
uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
 
UNIFORM - dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Uniform distribution.
UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils
 
UNIFORM_WEIGHTING - Static variable in class dev.nm.analysis.curvefit.LeastSquares
A uniform weighting
UniformDistributionOverBox - Class in dev.nm.stat.random.rng.multivariate
This random vector generator uniformly samples points over a box region.
UniformDistributionOverBox(RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
Constructs a random vector generator to uniformly sample points over a box region.
UniformDistributionOverBox(RandomLongGenerator, RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox
Constructs a random vector generator to uniformly sample points over a box region.
UniformDistributionOverBox1 - Class in dev.nm.solver.multivariate.initialization
This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.
UniformDistributionOverBox1(RandomLongGenerator, int, RealInterval...) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1
Construct a generator to uniformly sample points over a feasible region.
UniformDistributionOverBox2 - Class in dev.nm.solver.multivariate.initialization
This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.
UniformDistributionOverBox2(double, RealInterval[], int[], RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
Construct a generator to uniformly sample points over a feasible region.
UniformDistributionOverBox2(double, RealInterval[], int, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2
Construct a generator to uniformly sample points over a feasible region.
UniformMeshOverRegion - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
The initial population is generated by putting a uniform mesh/grid/net over the entire region.
UniformMeshOverRegion(RealScalarFunction, SimpleCellFactory, RandomLongGenerator, int, Vector[], double) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion
Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire region.
UniformRNG - Class in dev.nm.stat.random.rng.univariate.uniform
A pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval.
UniformRNG() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
Construct a pseudo uniform random number generator.
UniformRNG(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
Construct a seeded pseudo uniform random number generator.
UniformRNG(UniformRNG.Method) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG
Construct a pseudo uniform random number generator.
UniformRNG.Method - Enum in dev.nm.stat.random.rng.univariate.uniform
the pseudo uniform random number generators available
Uniroot - Interface in dev.nm.analysis.root.univariate
A root-finding algorithm is a numerical algorithm for finding a value x such that f(x) = 0, for a given function f.
UnitGrid - Class in dev.nm.stat.stochasticprocess.timegrid
This is the sequence of time points [0, 1, ..., T].
UnitGrid() - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
Construct a sequence of time points [0, 1, ..., ∞].
UnitGrid(int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid
Construct a sequence of time points [0, 1, ..., T].
unitRoundOff() - Static method in class dev.nm.misc.Constants
Get the default unit round off.
unitRoundOff(int, int) - Static method in class dev.nm.misc.Constants
Get the unit round off as defined in the reference.
UnivariateEVD - Interface in dev.nm.stat.evt.evd.univariate
Distribution of extreme values (e.g., maxima, minima, or other order statistics).
UnivariateMinimizer - Interface in dev.nm.root.univariate
A univariate minimizer minimizes a univariate function.
UnivariateMinimizer.Solution - Interface in dev.nm.root.univariate
This is the solution to a univariate minimization problem.
UnivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1.univariate
A univariate real function takes one real argument and outputs one real value.
UnivariateTimeSeries<T extends Comparable<? super T>,​E extends UnivariateTimeSeries.Entry<T>> - Interface in dev.nm.stat.timeseries.datastructure.univariate
This is a univariate time series indexed by some notion of time.
UnivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.univariate
This is the TimeSeries.Entry for a univariate time series.
UnivariateTimeSeriesUtils - Class in dev.nm.stat.timeseries.datastructure.univariate
These are the utility functions to manipulate a univariate time series.
UnsatisfiableErrorCriterionException - Exception in dev.nm.analysis.differentialequation
An exception that is thrown when the error criterion cannot be met.
UnsatisfiableErrorCriterionException() - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException
 
UnsatisfiableErrorCriterionException(String) - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException
 
UP - dev.nm.number.DoubleUtils.RoundingScheme
Always round up.
UP - tech.nmfin.meanreversion.hvolatility.Kagi.Trend
 
update(double) - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel
 
update(LocalDateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift
 
update(LocalDateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MeanPriceEstimator
Updates the model with the current price.
update(LocalDateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MRModel
Updates the model with the current price.
update(LocalDateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged
 
updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation
Update the Hessian matrix using the latest iterates.
updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
 
updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2
 
updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation
Update the Hessian matrix using the latest iterates.
updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1
Update the Hessian matrix using the latest iterates.
updateHessianInverse(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.DFPMinimizer
Sk+1 = Sk + δδ' / γ'δ - Sγγ'S' / γ'Sγ
updateHessianInverse1(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
Sk+1 = Sk + (1 + γ'Sγ/γ'δ)/γ'δ * δδ' -(δγ'S + Sγδ') / γ'δ, where S = H-1
updateHessianInverse2(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer
P + γγ' / γ'δ - P %*% γγ' %*% P / γ'Pδ, where P = S-1 is the Hessian.
updateStates() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
Update the bracketing interval and the best min found so far.
updateStates() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
 
upper() - Method in class dev.nm.interval.RealInterval
Get the upper bound of this interval.
upper() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound
 
UPPER - dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
an upper bi-diagonal matrix, where there are only non-zero entries on the main and super diagonal
UPPER - dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
diverge near x = b
upperBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem
Gets the upper bounds.
upperBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Split the box constraints and get the less-than-the-upper-bounds part.
UpperBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear
This is an upper bound constraints such that for all xi's, xi ≤ b
UpperBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.UpperBoundConstraints
Construct an upper bound constraints for all variables in a function.
upperBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints
Gets the upper bounds.
UpperTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
An upper triangular matrix has 0 entries where row index is greater than column index.
UpperTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
Constructs an upper triangular matrix from a 2D double[][] array.
UpperTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
Constructs an upper triangular matrix of dimension dim * dim.
UpperTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
Constructs an upper triangular matrix from a matrix.
UpperTriangularMatrix(UpperTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
Copy constructor.
Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
 
Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
 
Ut() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
Get the transpose of U, i.e., U().t().
Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Returns the matrix U' as in A=UDV'.

V

v - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext
The defining vector which is perpendicular to the Householder hyperplane.
v() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial
Get v as in (x2 + ux + v).
v() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer
When the problem is unbounded, the direction of arbitrarily negative can be computed by adjusting λ.
v() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2
 
V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization
Gets V, where V' = Vk * ...
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos
 
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder
Gets V, where V' = Vk * ...
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD
 
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD
 
V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition
Get the V matrix as in SVD decomposition.
V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD
Returns the matrix V as in A=UDV'.
V() - Method in class dev.nm.stat.factor.pca.PCAbyEigen
Gets the correlation (or covariance) matrix used by the PCA.
V() - Method in class tech.nmfin.returns.moments.ReturnsMoments
Gets the second moment matrix.
V(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Gets V(t), the covariance matrix of vt.
V(int) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Get V(t), the variance of vt.
validate(String) - Static method in class dev.nm.misc.license.Package
 
validateAll(String...) - Static method in class dev.nm.misc.license.Package
 
validateAny(String...) - Static method in class dev.nm.misc.license.Package
 
validateVersion(String, String) - Static method in class dev.nm.misc.license.Package
Check if a package is licensed up to a specified version.
value - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
the entry value
value() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
Gets the value of this entry.
value() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct
Get the value of the inner product.
value() - Method in class dev.nm.analysis.integration.univariate.Lebesgue
Get the integral value.
value() - Method in interface dev.nm.misc.algorithm.bb.BBNode
the value of this node
value() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode
 
value() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation
 
value() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation
 
value() - Method in class dev.nm.stat.descriptive.covariance.Covariance
 
value() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
 
value() - Method in class dev.nm.stat.descriptive.moment.Mean
 
value() - Method in class dev.nm.stat.descriptive.moment.Moments
 
value() - Method in class dev.nm.stat.descriptive.moment.Skewness
 
value() - Method in class dev.nm.stat.descriptive.moment.Variance
 
value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
 
value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
value() - Method in class dev.nm.stat.descriptive.rank.Max
 
value() - Method in class dev.nm.stat.descriptive.rank.Min
 
value() - Method in class dev.nm.stat.descriptive.rank.Quantile
 
value() - Method in interface dev.nm.stat.descriptive.Statistic
Get the value of the statistic.
value() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic
 
value() - Method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
Gets the value of this Mersenne exponent.
value() - Method in class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
Gets the estimator value (the mean).
value() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
Get the expectation (the mean).
value() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
 
value(double) - Method in class dev.nm.stat.descriptive.rank.Quantile
Compute the sample value corresponding to a quantile.
value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
 
value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers
 
value(double[]) - Method in interface dev.nm.stat.evt.exi.ExtremalIndexEstimation
Estimate the extremal index with the given observations from a distribution.
value(double[]) - Method in interface tech.nmfin.portfoliooptimization.corvalan2005.diversification.DiversificationMeasure
Evaluates the level of portfolio diversification given portfolio weights.
value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights
 
value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights
 
value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights
 
value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW
 
value(double[], double[], double[]) - Method in class dev.nm.stat.regression.WeightedRSS
Computes the weighted RSS for a set of observations.
value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.EdgeBetweeness
Gets the edge-betweeness of an edge.
value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.GirvanNewman
Get the edge-betweeness of an edge.
value(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral
Integrate the function with respect to a given filtration.
ValueArray(int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
 
valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.Dfdx.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.function.special.gamma.LogGamma.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.graph.algorithm.traversal.DFS.Node.Color
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.interval.IntervalRelation
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Quantile.QuantileType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Rank.TiesMethod
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
Deprecated.
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.TrendType
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.test.variance.Levene.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum tech.nmfin.meanreversion.hvolatility.Kagi.Trend
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum tech.nmfin.returns.ReturnsCalculators
Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
Returns the enum constant of this type with the specified name.
values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray
 
values() - Static method in enum dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.analysis.differentiation.univariate.Dfdx.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.analysis.differentiation.univariate.FiniteDifference.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.analysis.function.special.gamma.LogGamma.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class dev.nm.combinatorics.Ties
Get the numbers of occurrences of the objects.
values() - Static method in enum dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.graph.algorithm.traversal.DFS.Node.Color
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.interval.IntervalRelation
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.descriptive.rank.Quantile.QuantileType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.descriptive.rank.Rank.TiesMethod
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row
Gets the values of the row.
values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type
Deprecated.
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.timeseries.adf.TrendType
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.test.variance.Levene.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum tech.nmfin.meanreversion.hvolatility.Kagi.Trend
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum tech.nmfin.returns.ReturnsCalculators
Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum tech.nmfin.signal.infantino2010.Infantino2010Regime.Regime
Returns an array containing the constants of this enum type, in the order they are declared.
VanDerWaerden - Class in dev.nm.stat.test.rank
The Van der Waerden test tests for the equality of all population distribution functions.
VanDerWaerden(double[]...) - Constructor for class dev.nm.stat.test.rank.VanDerWaerden
Perform the Van Der Waerden test to test for the equality of all population distribution functions.
VanDerWaerden1969 - Class in dev.nm.stat.random.rng.univariate.beta
Deprecated.
Cheng1978 is a much better algorithm.
VanDerWaerden1969(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
Deprecated.
Construct a random number generator to sample from the beta distribution.
VanDerWaerden1969(RandomGammaGenerator, RandomGammaGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969
Deprecated.
Construct a random number generator to sample from the beta distribution.
var - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam
 
var() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
Gets the variance of the elements.
var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
Gets the variance of the prediction.
var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets the mean squared error of the h-step ahead prediction.
var() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit
Get the variance of the white noise.
var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets the mean squared error of the h-step ahead prediction.
var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Gets the mean squared error of the one-step ahead prediction.
var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares
 
var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel
Compute the unconditional variance of the GARCH model.
var(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix
Gets the variance of the i-th element.
var(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Gets the mean squared error of the prediction at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).
var(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
Gets the mean squared error for prediction errors at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).
VARFit - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
This class construct a VAR model by estimating the coefficients using OLS regression.
VARFit(MultivariateIntTimeTimeSeries, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARFit
Estimate a VAR model from a multivariate time series.
Variable(String, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
Constructs a named variable.
variance() - Method in class dev.nm.stat.descriptive.moment.Kurtosis
Get the (unbiased) variance.
variance() - Method in class dev.nm.stat.descriptive.moment.Skewness
Get the (unbiased) variance.
variance() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.FDistribution
Gets the variance of this distribution.
variance() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
 
variance() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution
Gets the variance of this distribution.
variance() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.TDistribution
Gets the variance of this distribution.
variance() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
 
variance() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
 
variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
 
variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
\[ \frac{\sigma^2}{(1-\xi)^2(1-2\xi)} \] for \(\xi < 1/2\).
variance() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
 
variance() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
 
variance() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
 
variance() - Method in interface dev.nm.stat.random.Estimator
Gets the variance of the estimator.
variance() - Method in class dev.nm.stat.random.sampler.resampler.BootstrapEstimator
Gets the estimator variance, of which the convergence limit is decided by sample size, not B.
variance() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation
Compute the variance of the integral.
variance() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime
Get the variance.
variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated.
variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated.
variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated.
variance() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution
Deprecated.
variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial
 
variance(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution
The variance function of the distribution in terms of the mean μ.
variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma
 
variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian
 
variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian
 
variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson
 
Variance - Class in dev.nm.stat.descriptive.moment
The variance of a sample is the average squared deviations from the sample mean.
Variance() - Constructor for class dev.nm.stat.descriptive.moment.Variance
Construct an empty Variance calculator.
Variance(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Variance
Construct an unbiased Variance calculator.
Variance(double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.Variance
Construct a Variance calculator, initialized with a sample.
Variance(Variance) - Constructor for class dev.nm.stat.descriptive.moment.Variance
Copy constructor.
VariancebtX - Class in dev.nm.algebra.linear.matrix.doubles.operation
Computes \(b'Xb\).
VariancebtX(Vector, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.VariancebtX
Computes \(b'Xb\).
variation() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
Gets the H variation/fluctuation.
VARIMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.arima
An ARIMA(p, d, q) process, Yt, is such that \[ X_t = (1 - L)^d Y_t \] where L is the lag operator, d the order of difference, Xt an ARMA(p, q) process, for which \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \] Xt, μ and εt are n-dimensional vectors.
VARIMAModel(Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Construct a multivariate ARIMA model with unit variance and zero-intercept (mu).
VARIMAModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Construct a multivariate ARIMA model with zero-intercept (mu).
VARIMAModel(Vector, Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Construct a multivariate ARIMA model with unit variance.
VARIMAModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Construct a multivariate ARIMA model.
VARIMAModel(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Copy constructor.
VARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel
Construct a multivariate model from a univariate ARIMA model.
VARIMASim - Class in dev.nm.stat.timeseries.linear.multivariate.arima
This class simulates a multivariate ARIMA process.
VARIMASim(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
Construct a multivariate ARIMA model, using random standard Gaussian innovations.
VARIMASim(VARIMAModel, Vector[], Vector[], RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
Construct a multivariate ARIMA model.
VARIMASim(VARIMAModel, RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim
Construct a multivariate ARIMA model.
VARIMAXModel - Class in dev.nm.stat.timeseries.linear.multivariate.arima
The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.
VARIMAXModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).
VARIMAXModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Construct a multivariate ARIMAX model with zero-intercept (mu).
VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Construct a multivariate ARIMAX model with unit variance.
VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Construct a multivariate ARIMAX model.
VARIMAXModel(VARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Copy constructor.
VARIMAXModel(ARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel
Construct a multivariate ARIMAX model from a univariate ARIMAX model.
VARLinearRepresentation - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.
VARLinearRepresentation(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
Construct the linear representation of an ARMA model up to the default number of lags VARLinearRepresentation.DEFAULT_NUMBER_OF_LAGS.
VARLinearRepresentation(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation
Construct the linear representation of an ARMA model.
VARMAAutoCorrelation - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
VARMAAutoCorrelation(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
Compute the auto-correlation function for a vector ARMA model.
VARMAAutoCovariance - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.
VARMAAutoCovariance(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
Compute the auto-covariance function for a vector ARMA model.
VARMAForecastOneStep - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
This is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor.
VARMAForecastOneStep(MultivariateIntTimeTimeSeries, VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
Construct an instance of InnovationAlgorithm for a multivariate ARMA time series.
VARMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
A multivariate ARMA model, Xt, takes this form.
VARMAModel(Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
VARMAModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Construct a multivariate ARMA model with zero-intercept (mu).
VARMAModel(Vector, Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Construct a multivariate ARMA model with unit variance.
VARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Construct a multivariate ARMA model.
VARMAModel(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Copy constructor.
VARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel
Construct a multivariate model from a univariate ARMA model.
VARMAXModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.
VARMAXModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).
VARMAXModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Construct a multivariate ARMAX model with zero-intercept (mu).
VARMAXModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Construct a multivariate ARMAX model with unit variance.
VARMAXModel(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Construct a multivariate ARMAX model.
VARMAXModel(VARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Copy constructor.
VARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel
Construct a multivariate model from a univariate ARMAX model.
VARModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
This class represents a VAR model.
VARModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Construct a VAR model with unit variance and zero-intercept (mu).
VARModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Construct a VAR model with zero-intercept (mu).
VARModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Construct a VAR model with unit variance.
VARModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Construct a VAR model.
VARModel(VARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Copy constructor.
VARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel
Construct a multivariate model from a univariate AR model.
VARXModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes this form.
VARXModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX model with unit variance and zero-mean.
VARXModel(Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX model with zero-mean.
VARXModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX model with unit variance.
VARXModel(Vector, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX model.
VARXModel(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Copy constructor.
VARXModel(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX(p) from a long-run VECM(p).
VARXModel(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel
Construct a VARX(p) from a transitory VECM(p).
VECM - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
A Vector Error Correction Model (VECM(p)) has one of the following specifications:
VECM(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Construct a VECM(p) model.
VECM(VECM) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM
Copy constructor.
VECMLongrun - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
The long-run Vector Error Correction Model (VECM(p)) takes this form.
VECMLongrun(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
Construct a long-run VECM(p) model with zero-intercept (mu).
VECMLongrun(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
Construct a long-run VECM(p) model.
VECMLongrun(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
Construct a long-run VECM(p) from a VARX(p).
VECMLongrun(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun
Copy constructor.
VECMTransitory - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
A transitory Vector Error Correction Model (VECM(p)) takes this form.
VECMTransitory(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
Construct a transitory VECM(p) model with zero-intercept (mu).
VECMTransitory(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
Construct a transitory VECM(p) model.
VECMTransitory(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
Construct a transitory VECM(p) from a VARX(p).
VECMTransitory(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory
Copy constructor.
Vector - Interface in dev.nm.algebra.linear.vector.doubles
An Euclidean vector is a geometric object that has both a magnitude/length and a direction.
VectorAccessException - Exception in dev.nm.algebra.linear.vector
This is the exception thrown when any invalid access to a Vector instance is detected, e.g., out-of-range index.
VectorAccessException(int, int) - Constructor for exception dev.nm.algebra.linear.vector.VectorAccessException
Constructs an instance of VectorAccessException for out-of-range access.
VectorAccessException(String) - Constructor for exception dev.nm.algebra.linear.vector.VectorAccessException
Constructs an instance of VectorAccessException.
VectorFactory - Class in dev.nm.algebra.linear.vector.doubles.operation
These are the utility functions that create new instances of vectors from existing ones.
VectorMathOperation - Class in dev.nm.algebra.linear.vector.doubles.dense
This is a generic implementation of the math operations of double based Vector.
VectorMathOperation() - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation
 
VectorMonitor - Class in dev.nm.misc.algorithm.iterative.monitor
This IterationMonitor stores all vectors generated during iterations.
VectorMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
 
VectorSizeMismatch - Exception in dev.nm.algebra.linear.vector
This is the exception thrown when an operation is performed on two vectors with different sizes.
VectorSizeMismatch(int, int) - Constructor for exception dev.nm.algebra.linear.vector.VectorSizeMismatch
Constructs an instance of SizeMismatch.
VectorSpace<V,​F extends Field<F>> - Interface in dev.nm.algebra.structure
A vector space is a set V together with two binary operations that combine two entities to yield a third, called vector addition and scalar multiplication.
vers(double) - Static method in class dev.nm.geometry.TrigMath
Returns the versed sine or versine of an angle.
vertex() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
Gets the node.
VertexTree<T> - Class in dev.nm.graph.type
A VertexTree is both a tree and a vertex/node.This implementation builds a tree incrementally and recursively (combining trees).
VertexTree(T) - Constructor for class dev.nm.graph.type.VertexTree
 
vertices() - Method in interface dev.nm.geometry.polyline.PolygonalChain
Get a list of the vertices defining the chain.
vertices() - Method in class dev.nm.geometry.polyline.PolygonalChainByArray
 
vertices() - Method in interface dev.nm.graph.Graph
Gets the set of all vertices in this graph.
vertices() - Method in interface dev.nm.graph.HyperEdge
Gets the set of vertices associated with the edge.
vertices() - Method in class dev.nm.graph.type.SimpleArc
 
vertices() - Method in class dev.nm.graph.type.SimpleEdge
 
vertices() - Method in class dev.nm.graph.type.SparseDiGraph
Gets the set of all vertices in this graph, sorted by the number of parents.
vertices() - Method in class dev.nm.graph.type.SparseGraph
 
vertices() - Method in class dev.nm.graph.type.SparseTree
 
vertices() - Method in class dev.nm.graph.type.VertexTree
 
visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction
 
visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction
 
visitingTemperature(int) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction
Gets the visiting temperature \(T^V_t\) at time t.
visitTime() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node
Gets the first visit time of this node.
Viterbi - Class in dev.nm.stat.hmm
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models.
Viterbi(HiddenMarkovModel) - Constructor for class dev.nm.stat.hmm.Viterbi
Constructs an Viterbi algorithm for an HMM.
VMAInvertibility - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.
VMAInvertibility(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
Construct the inverse representation of an ARMA model up to the default number of lags VMAInvertibility.DEFAULT_NLAGS.
VMAInvertibility(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility
Construct the inverse representation of an ARMA model.
VMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
This class represents a multivariate MA model.
VMAModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Construct a multivariate MA model with unit variance and zero-mean.
VMAModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Construct a multivariate MA model with zero-mean.
VMAModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Construct a multivariate MA model with unit variance.
VMAModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Construct a multivariate MA model.
VMAModel(VMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Copy constructor.
VMAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel
Construct a multivariate MA model from a univariate MA model.
volatility() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
Gets the H volatility.
Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3
 
Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3
 
Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace
Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.

W

W(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Gets W(t), the covariance matrix of wt.
W(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Get W(t), the variance of wt.
W(Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
Compute W.
w0 - Variable in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights
 
wA() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the weighted regressor matrix.
WaveEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1
A one-dimensional wave equation is a hyperbolic PDE that takes the following form.
WaveEquation1D(double, double, double, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D
Constructs an one-dimensional wave equation.
WaveEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
A two-dimensional wave equation is a hyperbolic PDE that takes the following form.
WaveEquation2D(double, double, double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D
Create a two-dimensional wave equation.
WeibullDistribution - Class in dev.nm.stat.distribution.univariate
The Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2), where k is the shape parameter.
WeibullDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.WeibullDistribution
Construct a Weibull distribution.
WeibullRNG - Class in dev.nm.stat.random.rng.univariate
This random number generator samples from the Weibull distribution using the inverse transform sampling method.
WeibullRNG() - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG
Constructs a random number generator to sample from the Weibull distribution.
WeibullRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG
Constructs a random number generator to sample from the Weibull distribution.
weight() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint
 
WeightedArc<V> - Interface in dev.nm.graph
A weighted arc is an arc that has a weight or a cost associated with it.
WeightedEdge<V> - Interface in dev.nm.graph
A weighted edge has a weight or a cost associated with it.
weightedFittedValues() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the weighted, fitted values.
WeightedMean - Class in dev.nm.stat.descriptive.moment.weighted
The weighted mean is defined as \[ \bar{x} = \frac{ \sum_{i=1}^N w_i x_i}{\sum_{i=1}^N w_i} \]
WeightedMean() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
WeightedMean(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean
 
WeightedMedian - Class in dev.nm.stat.descriptive.moment.weighted
A weighted median of a sample is the 50% weighted percentile.It was first proposed by F.
WeightedMedian(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMedian
Finds the weighted median of an array.
weightedResiduals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals
Gets the weighted residuals.
WeightedRSS - Class in dev.nm.stat.regression
Weighted sum of squared residuals (RSS) for a given function \(f(.)\) and observations \((x_i,y_i)\).
WeightedRSS(UnivariateRealFunction) - Constructor for class dev.nm.stat.regression.WeightedRSS
Constructs a calculator to compute the weighted RSS for a given function.
WeightedVariance - Class in dev.nm.stat.descriptive.moment.weighted
The weighted sample variance is defined as follows.
WeightedVariance() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
WeightedVariance(boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
WeightedVariance(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
WeightedVariance(double[], double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
 
weights - Variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty
the weights for the constraints
weights() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting
Gets the weights assigned to the observations.
weights() - Method in class dev.nm.stat.regression.linear.glm.IWLS
 
weights() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson
 
weights() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the weights assigned to each observation.
weights() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights
 
which(double[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
Get the indices of the array elements which satisfy the boolean test.
which(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils
Get the indices of the array elements which satisfy the boolean test.
White - Class in dev.nm.stat.test.regression.linear.heteroskedasticity
The White test tests for conditional heteroskedasticity.
White(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.White
Perform the White test to test for heteroskedasticity in a linear regression model.
WHITE - dev.nm.graph.algorithm.traversal.DFS.Node.Color
not seen
WilcoxonRankSum - Class in dev.nm.stat.test.rank.wilcoxon
The Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset.
WilcoxonRankSum(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations.
WilcoxonRankSum(double[], double[], double) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
WilcoxonRankSum(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
WilcoxonRankSum(double[], double[], double, boolean, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum
Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.
WilcoxonRankSumDistribution - Class in dev.nm.stat.test.rank.wilcoxon
Compute the exact distribution of the Wilcoxon rank sum test statistic.
WilcoxonRankSumDistribution(int, int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution
Construct a Wilcoxon Rank Sum distribution for sample sizes M and N.
WilcoxonSignedRank - Class in dev.nm.stat.test.rank.wilcoxon
The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and for the two-sample case, the equality of medians of groups.
WilcoxonSignedRank(double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
WilcoxonSignedRank(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
WilcoxonSignedRank(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
WilcoxonSignedRank(double[], int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank
Perform the Wilcoxon Signed Rank test to test for the equality of medians.
WilcoxonSignedRankDistribution - Class in dev.nm.stat.test.rank.wilcoxon
Compute the exact distribution of the Wilcoxon signed rank test statistic.
WilcoxonSignedRankDistribution(int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Construct a Wilcoxon Signed Rank distribution for a sample size N.
withInitialGuess(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Overrides the initial guess of the solution.
withLeftPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Overrides the left preconditioner.
withMaxIteration(int) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Overrides the maximum count of iterations.
withNewLength(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm
 
withRightPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Overrides the right preconditioner.
withTolerance(Tolerance) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem
Overrides the tolerance instance.
Wt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt
Get the current value(s) of the driving Brownian motion(s).
Wt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt
Get the current value of the driving Brownian motion.
wy() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the weighted response vector.

X

x - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
This is the minimizer for the primal problem.
x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
x() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Get the values of the independent variable xi.
x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
x() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
Get a copy of the x's.
x() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
Get the values of the independent variable.
x() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
 
x() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
Get the abscissae.
x() - Method in class dev.nm.analysis.function.tuple.Pair
x
x() - Method in class dev.nm.analysis.function.tuple.PartialFunction
 
x() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
 
x() - Method in class dev.nm.analysis.function.tuple.Triple
Return the value of the first element of the triple.
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential
 
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential
 
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable
 
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable
 
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity
 
x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval
 
x() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule
the transformation: x(t)
x() - Method in exception dev.nm.analysis.root.univariate.NoRootFoundException
the best approximate root found so far
x() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome
Get the candidate solution.
x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState
Gets the position.
x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging
Gets the current position.
x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Get the value of xi, the i-th value of the independent variable x.
x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
 
x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
Get all the values of the independent variable xi as an array.
x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
 
x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Gets the value on the x-axis at index k.
x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D
Gets the value on the space axis at index j.
x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the value on the x-axis at index i.
x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
 
x(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
Get the value of the independent variable xi at the given index.
x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
 
X - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
This is the minimizer for the primal problem.
X() - Method in interface dev.nm.stat.factor.pca.PCA
Gets the (possibly centered and/or scaled) data matrix X used for the PCA.
X() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the factor matrix.
X() - Method in class tech.nmfin.signal.infantino2010.Infantino2010PCA.Signal
 
x_F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result
transform zeta_list back from abscissa; it is same as whole_x_list; just follow the R code.
x0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Get the value of x0, the first value of the independent variable x.
x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
Get the start point of the integrating interval [x0, x1].
x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Gets the start point of the integrating interval [x0, x1].
x0(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
Get the value of \(\mathbf{x_i}_0\), the first value of the independent variable \(x_i\).
x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
Get the end point of the integrating interval [x0, x1].
x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Gets the end point of the integrating interval [x0, x1].
xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast
Gets the prediction at time t.
xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
The next h-step ahead prediction.
xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep
Gets the h-step ahead prediction of the time series.
xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Gets the one-step ahead prediction of the time series.
xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
xHat(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
Gets the one-step ahead prediction \(\hat{x}_{n+1}\).
xi(HiddenMarkovModel, int[], ForwardBackwardProcedure) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch
Gets the ξ matrices, where for 1 ≤ t ≤ T - 1, the t-th entry of ξ is an (N * N) matrix, for which the (i, j)-th entry is ξt(i, j).
XiTanLiu2010a - Class in dev.nm.stat.random.rng.univariate.gamma
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
XiTanLiu2010a(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
Construct a random number generator to sample from the gamma distribution.
XiTanLiu2010a(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a
Construct a random number generator to sample from the gamma distribution.
XiTanLiu2010b - Class in dev.nm.stat.random.rng.univariate.gamma
Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.
XiTanLiu2010b(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
Construct a random number generator to sample from the gamma distribution.
XiTanLiu2010b(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b
Construct a random number generator to sample from the gamma distribution.
xl - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the lower bound of the bracketing interval
XL2Norm() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the L2 norms of the covariates (a vector of ones if no standardization is required).
XLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the matrix of covariates (possibly demeaned and/or scaled) to be used in LARS.
XMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the mean vector to be subtracted from the covariates (a vector of zeros if no intercept is included).
xmin - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the best minimizer found so far
xmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution
 
xnext - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the next best guess of the minimizer
xnext() - Method in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
Compute the next best estimate within the bracketing interval.
xnext() - Method in class dev.nm.root.univariate.bracketsearch.BrentMinimizer.Solution
 
xnext() - Method in class dev.nm.root.univariate.bracketsearch.FibonaccMinimizer.Solution
 
xnext() - Method in class dev.nm.root.univariate.bracketsearch.GoldenMinimizer.Solution
 
xShifted(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets the shifted time series of observations.
xt(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Evaluate the state equation without the control variable.
xt(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Evaluate the state equation.
xt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Evaluates the state equation without the control variable.
xt(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Evaluates the state equation.
Xt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the current value of the stochastic process.
Xt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Get the current value of the stochastic process.
xt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Predict the next state without control variable.
xt_mean(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Predict the next state.
xt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Predicts the next state without control variable.
xt_mean(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Predicts the next state.
xt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation
Get the variance of the apriori prediction for the next state.
xt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation
Gets the variance of the apriori prediction for the next state.
XtAdaptedFunction - Class in dev.nm.stat.stochasticprocess.univariate.sde
This represents an Ft-adapted function that depends only on X(t).
XtAdaptedFunction() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
 
xu - Variable in class dev.nm.root.univariate.bracketsearch.BracketSearchMinimizer.Solution
the upper bound of the bracketing interval

Y

y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath
This is the maximizer for the dual problem.
y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution
This is the maximizer for the dual problem.
y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
y() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Get the values of the independent variable yj.
y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
y() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution
Get the corresponding values of the dependent variable.
y() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
 
y() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs
Get the ordinates.
y() - Method in class dev.nm.analysis.function.tuple.Pair
y
y() - Method in class dev.nm.analysis.function.tuple.PartialFunction
 
y() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs
 
y() - Method in class dev.nm.analysis.function.tuple.Triple
Return the value of the second element of the triple.
y() - Method in class dev.nm.stat.regression.linear.LMProblem
Gets the response vector, the regressands, the dependent variables.
y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
y(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Get the value of yj, the j-th value of the independent variable y.
y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D
Gets the value on the y-axis at index j.
y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D
Get the value on the y-axis at index j.
y(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep
Gets the stationary arma series.
y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid
 
y(int...) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid
Get the value of the dependent variable y at the given indices in the grid.
y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid
 
y0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
Get the value of y0, the first value of the independent variable y.
y0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder
Gets the initial value of y, that is, y0.
y0(int) - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE
Get the initial value for the n-th order (derivative).
yearMonthPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
 
yearPanelData(String, String, String[]) - Static method in class dev.nm.stat.regression.linear.panel.PanelData
 
yes(double) - Method in interface dev.nm.number.DoubleUtils.ifelse
Return value for a true element of test.
yLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the vector of response variable (possibly demeaned) to be used in LARS.
yMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem
Gets the mean to be subtracted from the response variable (0 if no intercept is included).
yt(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Evaluate the observation equation.
yt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Evaluates the observation equation.
yt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Predict the next observation.
yt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Predicts the next observation.
yt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation
Get the variance of the apriori prediction for the next observation.
yt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation
Gets the covariance of the apriori prediction for the next observation.

Z

z() - Method in class dev.nm.analysis.function.tuple.Triple
Return the value of the third element of the triple.
z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid
 
z(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid
Get the value of the dependent variable z at the given indices in the grid.
z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid
 
Z1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
Get Z1.
Z2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino
Get Z1.
ZangwillImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl
 
ZangwillMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
Zangwill's algorithm is an improved version of Powell's algorithm.
ZangwillMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer
Construct a multivariate minimizer using the Zangwill method.
ZangwillMinimizer.ZangwillImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
an implementation of Zangwill's algorithm
ZERO - Static variable in class dev.nm.analysis.function.polynomial.Polynomial
a polynomial representing 0
ZERO - Static variable in class dev.nm.number.complex.Complex
a number representing 0.0 + 0.0i
ZERO - Static variable in class dev.nm.number.Real
a number representing 0
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
 
ZERO() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing
Get a zero matrix that has the same dimension as this matrix.
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
Deprecated.
no zero matrix for GivensMatrix
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
 
ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
 
ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
 
ZERO() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector
Get a 0-vector that has the same length as this vector.
ZERO() - Method in interface dev.nm.algebra.structure.AbelianGroup
The additive element 0 in the group, such that for all elements a in the group, the equation 0 + a = a + 0 = a holds.
ZERO() - Method in class dev.nm.analysis.function.polynomial.Polynomial
 
ZERO() - Method in class dev.nm.number.complex.Complex
Get zero - the number representing 0.0 + 0.0i.
ZERO() - Method in class dev.nm.number.Real
 
ZERO_ENTRY - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
 
ZeroDriftVector - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
This class represents a 0 drift function.
ZeroDriftVector() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
 
ZeroPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod
This is a dummy zero cost (no cost) penalty function.
ZeroPenalty(int) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
Construct a no-cost penalty function.
Ziggurat2000 - Class in dev.nm.stat.random.rng.univariate.normal
The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.
Ziggurat2000() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
Construct a Ziggurat random normal generator.
Ziggurat2000(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000
Construct a Ziggurat random normal generator.
Ziggurat2000Exp - Class in dev.nm.stat.random.rng.univariate.exp
This implements the ziggurat algorithm to sample from the exponential distribution.
Ziggurat2000Exp() - Constructor for class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp
 
Zignor2005 - Class in dev.nm.stat.random.rng.univariate.normal
This is an improved version of the Ziggurat algorithm as proposed in the reference.
Zignor2005() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005
Construct an improved Ziggurat random normal generator.
Zignor2005(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005
Construct an improved Ziggurat random normal generator.
Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess
Get a d-dimensional Gaussian innovation.
Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt
Get the current value of the Gaussian innovation.
Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess
Get a Gaussian innovation.
Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft
Get the current value of the Gaussian innovation.
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