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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).
Elem