Index (NM DEV API (v1.0.0) by NM)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

A

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.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(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials: Compute a_n.
a() - Method in class dev.nm.analysis.function.special.gaussian.Gaussian: Get a.
A() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints: Get the constraint coefficients.
A - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution: This is either [A] or [ A] [-C]
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem: Gets A_i.
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem: Gets A_i.
A(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem: Gets A_i.
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 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 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
A() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
a() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
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 - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix: a random matrix constructed by AS159
A() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Gets A as in eq.
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(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation: Computes the absolute values of a vector, element-by-element.
abs(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath: Get the absolute values.
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, 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.
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.
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, Collection<Integer>) - 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.
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(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(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(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(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(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(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
add(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
add(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
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(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(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
add(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
add(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
add(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation: Adds two vectors, element-by-element.
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) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
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(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector: \(this + that\)
add(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector: Add a constant to all entries in this vector.
add(G) - Method in interface dev.nm.algebra.structure.AbelianGroup: + : G × G → G
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(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(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(double, T) - Method in class dev.nm.misc.algorithm.Bins: Add a valued item to the bin.
add(T) - Method in class dev.nm.misc.datastructure.IdentityHashSet
add(Complex) - Method in class dev.nm.number.complex.Complex
add(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation
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 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(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.
addActive(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet: Add active indices.
addActive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet: Add active indices.
addActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet: Add an active constraint by index.
addAll(Collection<? extends T>) - Method in class dev.nm.misc.datastructure.IdentityHashSet
addCheck(PairingCheck) - Method in class tech.nmfin.meanreversion.cointegration.RobustCointegration
addColAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable: Adds a column at i.
addColAt(int) - 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(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.
addData(double...) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation: Update the statistic with more data.
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation: Add the given two samples.
addData(double...) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation: Update the statistic with more data.
addData(double[], double[]) - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation: Add the given two samples.
addData(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.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[], double[]) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean
addData(double...) - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance
addData(double[], 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
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
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(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree
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(Collection<Integer>) - Method in class dev.nm.misc.algorithm.ActiveSet: Add inactive indices.
addInactive(int[]) - Method in class dev.nm.misc.algorithm.ActiveSet: Add inactive indices.
addInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet: Add an inactive constraint by index.
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
addIterate(Vector) - Method in class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor
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(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation: Row addition: A[i₁, ] = A[i₁, ] + c * A[i₂, ]
addRow(double, double[]) - Method in class dev.nm.misc.datastructure.MathTable: Adds a row to the table.
addRow(S, String, double...) - Method in class dev.nm.stat.regression.linear.panel.PanelData: Inserts a row of data into the panel.
addRow(PanelData<S>.Row) - Method in class dev.nm.stat.regression.linear.panel.PanelData: Inserts a row of data into the panel.
addRowAt(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable: Adds a row at i.
addRowAt(int) - 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(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
addVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
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, int, int, long) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution: Construct an asymptotic distribution for the augmented Dickey-Fuller 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.
ADFAsymptoticDistribution1 - Class in dev.nm.stat.test.timeseries.adf: Deprecated.
use instead ADFAsymptoticDistribution
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(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.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, 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.
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) - Constructor for class dev.nm.stat.test.timeseries.adf.ADFFiniteSampleDistribution: Construct a finite sample distribution for the original Dickey-Fuller test statistic.
ADFFiniteSampleDistribution(int) - 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: A_eq 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.
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(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
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.
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(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.
alpha() - Method in class dev.nm.stat.cointegration.CointegrationMLE: Get the set of adjusting coefficients, by columns.
alpha - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda: α: the shape parameter
alpha() - Method in class dev.nm.stat.regression.linear.panel.FixedEffectsModel: Gets the individual/subject specific terms.
alpha(double) - Method in class dev.nm.stat.test.distribution.AndersonDarlingPValue: Gets the p-value for a test statistic.
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.
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.
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(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, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation: Computes the angle between two vectors.
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(H) - Method in interface dev.nm.algebra.structure.HilbertSpace: ∠ : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
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(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.
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, UnivariateRealFunction) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates: Estimate \(E(f(X_1))\), where f is a function of a random variable.
AntitheticVariates(UnivariateRealFunction, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.AntitheticVariates: Estimate \(E(f(X_1))\) and use AntitheticVariates.INVERSE as the default antithetic path.
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
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 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.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, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils: Checks if two vectors are orthogonal, i.e., v1 ∙ v2 == 0.
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.
areOrthogonormal(Vector, Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils: Checks if two vectors are orthogonormal.
areOrthogonormal(Vector[], double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils: Checks if a set of 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, ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast: Constructs a forecaster for a time series assuming ARIMA model.
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.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, X_t, is such that \[ (1 - B)^d X_t = Y_t \] where B is the backward or lag operator, d the order of difference, Y_t 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, double[], int, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAModel: Construct a univariate ARIMA model.
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[], 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[], 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(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, 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.
ARIMASim(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim: Construct an ARIMA model, using random standard Gaussian innovations.
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, double[], int, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel: Construct a univariate ARIMAX model.
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[], 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[], 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(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, ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast: Constructs a forecaster for a time series assuming ARMA model.
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.
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(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.
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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep: Makes the h-step ahead prediction for an ARMA model.
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.
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(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.
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(double[], ARMAModel) - 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, double, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.ARMAGARCHFit: Constructs a model.
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.
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, X_t, takes this form.
ARMAModel(double, double[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel: Construct a univariate ARMA model.
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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel: Construct a univariate ARMA model with zero-intercept (mu).
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(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel: Copy constructor.
armaxMean(Matrix, Matrix, Vector) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel: Compute the multivariate ARMAX conditional mean.
armaxMean(double[], double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel: Compute the univariate 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[], double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAXModel: Construct a univariate ARMAX model.
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) - 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[]) - 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(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, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel: Construct a univariate AR model.
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) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARModel: Construct a univariate AR model with zero-intercept (mu).
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(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(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
ARResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ARResamplerFactory
ARTIFICIAL - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
ARTIFICIAL_COST - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
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.
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.
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[], 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.
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.
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[], 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.
AutoARIMAFit(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit: Automatically selects and estimates the ARIMA model using default 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 EX_t = 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 {x_t}.
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 {x_t}.
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
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, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.AverageImplicitModelPCA: Constructs an explicit-implicit model for a time series of vectored observations
AverageImplicitModelPCA(Matrix, double) - 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 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.linearsystem.LSProblem: Gets the non-homogeneous part, the right-hand side vector, of the linear system.
B() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow: Get the double precision matrix.
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(int, double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.Partials: Compute b_n.
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.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 - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable
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 class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result: Gets B, the implicit factor loading matrix.
b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result: Gets the implicit factor loading for the n-th subject.
B() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result: Gets B, the factor loading matrix.
b(int) - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result: Gets the factor loading for the n-th subject.
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 interface dev.nm.stat.random.variancereduction.ControlVariates.Estimator: Gets the optimal b.
B(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration: Get the Brownian motion value at the i-th time point.
B() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal
B() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Gets B as in eq.
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.
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.
base() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow: Get the radix or base of the coefficient.
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.
begin() - Method in class dev.nm.interval.Interval: Get the beginning of this interval.
BEGINNING_OF_TIME - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils: This represents a time before all (representable) times.
BEGINNING_OF_TIME_LONG - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils: This represents a time before all (representable) times, in long representation.
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: b_eq 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() - 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 - Class in dev.nm.analysis.function.special.beta: The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]
The R equivalent function is beta.
Beta() - Constructor for class dev.nm.analysis.function.special.beta.Beta
beta() - Method in class dev.nm.stat.cointegration.CointegrationMLE: Get the set of cointegrating factors, by columns.
beta(int) - Method in class dev.nm.stat.cointegration.CointegrationMLE: Get the r-th cointegrating factor, counting from 1.
beta - Variable in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda: β: the shape parameter
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 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair
beta() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
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: \[ I_x(p,q) = \frac{B(x;\,p,q)}{B(p,q)} = \frac{1}{B(p,q)} \int_0^x t^{p-1}\,(1-t)^{q-1}\,dt, p > 0, q > 0 \]
The R equivalent function is pbeta.
BetaRegularized(double, double) - Constructor for class dev.nm.analysis.function.special.beta.BetaRegularized: Construct an instance of I_x(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: \[ x = I^{-1}_{(p,q)}(u), 0 \le u \le 1 \]
The R equivalent function is qbeta.
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(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.
BiconjugateGradientSolver(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(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) .
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) .
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: Create a new instance which computes the partial derivatives using PartialDerivativesByCenteredDifferencing.
BicubicInterpolation(BicubicInterpolation.PartialDerivatives) - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation: Create 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, 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, 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.
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, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.BinomialRNG: Construct a random number generator to sample 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.
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.
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
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: Create 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: Create 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: Create 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: Create 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.
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(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE: Construct a univariate Brownian motion.
BMSDE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.BMSDE: Construct a univariate standard 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> - 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(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.BoxMuller: Construct a random number generator to sample from the standard Normal distribution.
BoxMuller() - 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, BoxConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem: Constructs an optimization problem with box constraints.
BoxOptimProblem(RealScalarFunction, Vector, Vector) - 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.
use LjungBox
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.solver.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.solver.univariate.bracketsearch.BracketSearchMinimizer: Construct a univariate minimizer using a bracket search method.
BracketSearchMinimizer.Solution - Class in dev.nm.solver.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.
BrentCetaMaximizer - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer: Searches for the maximal point of C(η) by Brent's method.
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).
BrentCetaMaximizer() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer: Constructs a maximizer using the default epsilon (for the Brent's search algorithm).
BrentMinimizer - Class in dev.nm.solver.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.solver.univariate.bracketsearch.BrentMinimizer: Construct a univariate minimizer using Brent's algorithm.
BrentMinimizer.Solution - Class in dev.nm.solver.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(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(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.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(double) - Method in class tech.nmfin.meanreversion.hvolatility.Kagi: Makes a KAGI construction for the given random process.
build() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver.Builder
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.

C

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.problem.SDPDualProblem: Gets C.
C() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem: Gets C.
c(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem: Gets c_i.
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 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 - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result: n=c/p
c() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter
c(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure
C() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Gets C as in eq.
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, RealVectorFunction, RntoMatrix) - 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) - 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, ..., X_n.
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[], RandomLongGenerator) - 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.
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>, RandomLongGenerator) - 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.
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(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory: Combines an array of vectors 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(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory: Combines a list of vectors by columns.
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.
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(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
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(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
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, 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(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
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).
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, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978: Construct a random number generator to sample from the beta distribution.
Cheng1978(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.Cheng1978: Construct a random number generator to sample from the beta distribution.
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
children(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
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, 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(Matrix) - 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 * L^t, where L is a lower triangular 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.
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) - 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 * L^t, 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 * L^t, 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 * L^t, 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.
clamped() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline: Creates 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: Creates 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
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, int, Matrix) - Constructor for class dev.nm.stat.cointegration.CointegrationMLE: Perform the Johansen MLE procedure on a multivariate time series.
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) - 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.
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(CetaMaximizer[]) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer: Constructs a combined maximizer.
CombinedCetaMaximizer() - 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, 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.
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) - 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.
compare(SparseMatrix.Entry, SparseMatrix.Entry) - Method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator
compare(SparseVector.Entry, SparseVector.Entry) - Method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator
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(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils: Compare two BigDecimals up to a precision.
compare(Number, double) - Method in class dev.nm.number.complex.Complex
compare(double, double, double) - Static method in class dev.nm.number.DoubleUtils: Compares two doubles up to a precision.
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.
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<S>.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, double) - Constructor for class dev.nm.number.complex.Complex: Construct a complex number from the real and imaginary parts.
Complex(double) - Constructor for class dev.nm.number.complex.Complex: Construct a complex number from a real number.
ComplexMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype: This is 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.
ComplexMatrix(double[][]) - 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(CompositeDoubleArrayOperation.ImplementationChooser) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation: Construct a CompositeDoubleArrayOperation by supplying the multiplexing criterion and the multiple DoubleArrayOperations.
CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class dev.nm.number.doublearray.CompositeDoubleArrayOperation: Construct a CompositeDoubleArrayOperation that chooses an implementation by array length.
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: Construct a linear congruential generator from some simpler and shorter modulus generators.
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.
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(double[][], double[][], int) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator: Compute the statistics.
compute() - Method in class tech.nmfin.meanreversion.daspremont2008.AhatEstimation
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(Vector...) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory: Concatenates an array of vectors into one vector.
concat(Collection<Vector>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory: Concatenates an array of vectors into one vector.
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(double[]...) - Static method in class dev.nm.number.DoubleUtils: Concatenate an array of arrays into one array.
concat(LinearConstraints...) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints: Concatenate collections of linear constraints into one collection.
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, 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.
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.
ConcurrentCachedRNG - Class in dev.nm.stat.random.rng.concurrent.cache: This is a fast thread-safe wrapper for random number generators.
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.
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.
ConcurrentCachedRVG - Class in dev.nm.stat.random.rng.concurrent.cache: This is a fast thread-safe wrapper for random vector generators.
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.
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.
ConcurrentStandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal
ConcurrentStandardNormalRNG(RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG
ConcurrentStandardNormalRNG() - 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(Matrix, Matrix) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel: Compute the multivariate ARMA conditional mean, given all the lags.
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(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model: Compute the univariate AR1 conditional mean, given the last lag.
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, 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) - 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 - 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.
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.
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 AA^t is symmetric and positive definite, and hence CG is applicable.
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.
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.
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 AA^t is symmetric and positive definite, and hence CG is applicable.
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.
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.
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(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.
ConjugateGradientSolver(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(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.
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.
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, 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.
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.
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> - 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 - Interface in dev.nm.solver.multivariate.constrained.constraint: A set of constraints for a (real-valued) optimization problem is a set of functions.
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.
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(V) - Method in class dev.nm.graph.type.SparseGraph: Check if this graph contains a vertex.
contains(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
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: \[ z = b_0 + \cfrac{a_1}{b_1 + \cfrac{a_2}{b_2 + \cfrac{a_3}{b_3 + \cfrac{a_4}{b_4 + \ddots\,}}}} \] a_i and b_i can be functions of x, which in turn makes z a function of x.
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(ContinuedFraction.Partials) - 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 a_n, and the partial denominators b_n.
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
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: This exception is thrown by IterativeLinearSystemSolver.solve(dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem, dev.nm.misc.algorithm.iterative.monitor.IterationMonitor) when the iterative algorithm detects a breakdown or fails to converge.
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(Matrix, double) - Static method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
cor(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair
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, double) - Constructor for class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck
CorrelationCheck(Matrix, 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, ..., X_n is the n × n matrix whose i,j entry is corr(X_i, X_j), the correlation between X₁ and X_n.
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 - 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, 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.
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.
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, 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.
CourantPenalty(EqualityConstraints) - 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 - 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.
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(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() - 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
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, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO: Estimate the covariance matrix directly by using LASSO.
CovarianceSelectionLASSO(CovarianceSelectionProblem) - 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(MultivariateTimeSeries, double, boolean) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem: Constructs a covariance selection problem from a multivariate time series.
CovarianceSelectionProblem(MultivariateTimeSeries, double) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem: Constructs a covariance selection problem from a multivariate time series.
CovarianceSelectionProblem(CovarianceSelectionProblem) - Constructor for class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem: Copy constructor.
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, 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) - 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 G^t * 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: Creates an instance with default end conditions which fits natural splines, meaning that the second derivative at both ends are zero.
cumsum(Vector[]) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory: Gets the cumulative sums.
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.
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) /]
This implementation uses the Beasley-Springer-Moro algorithm.
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.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.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint: Gets d.
D() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson: Computes 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 tech.nmfin.infantino2010.Infantino2010PCA.Signal
D() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Gets D as in eq.
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.ModelParam) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM
Dai2011HMM(Dai2011HMM) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM: Copy constructor.
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 DateTime and value can be any type.
DateTimeGenericTimeSeries(DateTime[], 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 DateTime.
DateTimeTimeSeries(DateTime[], double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries: Construct a time series from DateTime and double.
DateTimeTimeSeries(ArrayList<DateTime>, ArrayList<Double>) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.DateTimeTimeSeries: Construct a time series from DateTime and double.
dB(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess: 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.
dB(double) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess: Get a Brownian motion increment.
db(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE: \[ \frac{d\sigma}{dt} \]
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_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_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 - Ax_i 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 - Ax_i 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 - Ax_i 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 - Ax_i 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 - Ax_i 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 - Ax_i 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 - Ax_i 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 - Ax_i 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(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(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.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(double) - Constructor for class dev.nm.solver.multivariate.initialization.DefaultSimplex: Construct a simplex builder.
DefaultSimplex() - 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 - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr: Determines whether a sub-diagonal entry is sufficiently small to be neglected.
deflationCriterion - Variable in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg
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.
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, 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.
DenseData(double[], int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData: Construct a storage.
DenseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense: This class implements the standard, dense, double based matrix representation.
DenseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix: Constructs a 0 matrix of dimension nRows * nCols.
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(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix: Constructs a column matrix from a vector.
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.
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(int) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector.
DenseVector(int, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector, initialized by repeating a value.
DenseVector(double...) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector, initialized by a double[].
DenseVector(Double[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector, initialized by a Double[].
DenseVector(List<Double>) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector, initialized by a List<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(int[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector, initialized by a int[].
DenseVector(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Constructs a vector from a column or row matrix.
DenseVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Casts any vector to a DenseVector.
DenseVector(DenseVector) - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.DenseVector: Copy constructor.
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(Vector) - Method in class dev.nm.stat.distribution.multivariate.AbstractBivariateProbabilityDistribution
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(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
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, 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(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(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(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
DEOptim - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim: Differential Evolution (DE) is a global optimization method.
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(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(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.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(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
depth(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree
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.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.solver.univariate - package dev.nm.solver.univariate
dev.nm.solver.univariate.bracketsearch - package dev.nm.solver.univariate.bracketsearch
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(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
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.
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(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.
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.
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, Dfdx.Method) - Constructor for class dev.nm.analysis.differentiation.univariate.Dfdx: Construct the first order derivative function of a univariate function f.
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.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 - 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.
diagonalMatrix(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory: Gets the diagonal of a matrix.
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
diff(double[], int, int) - Static method in class dev.nm.number.DoubleUtils: Gets the lagged and iterated differences.
diff(double[]) - Static method in class dev.nm.number.DoubleUtils: Gets the first differences of an array.
diff(double[][], int, int) - Static method in class dev.nm.number.DoubleUtils: Gets the lagged and iterated differences of vectors.
diff(double[][]) - Static method in class dev.nm.number.DoubleUtils: Gets the first differences of an array of vectors.
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 x_t in discrete time t is the transformation of the series to a new time series (1-B)x_t where the new values are the differences between consecutive values of x_t.
DifferencedIntTimeTimeSeries(IntTimeTimeSeries, int) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries
Diffusion - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients: This class represents the diffusion term, σ, of a univariate SDE.
diffusion() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE: Get the diffusion coefficient: \(\sigma(t, X_t, Z_t, ...)\).
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.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 y_t.
dimension() - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation: Gets the dimension of state x_t.
dimension() - Method in class dev.nm.stat.dlm.univariate.ObservationEquation: Get the dimension of observation y_t.
dimension() - Method in class dev.nm.stat.dlm.univariate.StateEquation: Get the dimension of state x_t.
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.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.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.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.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.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[], double) - Constructor for class dev.nm.stat.distribution.multivariate.DirichletDistribution: Constructs an instance of Dirichlet distribution.
DirichletDistribution(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(double, double, double) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid.Discretization: Constructs a discretization of an interval.
discretization - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
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(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(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
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(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(F) - Method in interface dev.nm.algebra.structure.Field: / : F × F → F
That is the same as this.multiply(that.inverse())
divide(Complex) - Method in class dev.nm.number.complex.Complex: Compute the quotient of this complex number divided by another complex number.
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(Real) - Method in class dev.nm.number.Real
divide(Real, int) - Method in class dev.nm.number.Real: / : R × R → R
Divide this number by another one.
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, double[], RandomStandardNormalGenerator) - 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) - 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, 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, 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.
dotProduct(long[], long[]) - Static method in class dev.nm.analysis.function.FunctionOps: \(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
dotProduct(double[], double[]) - Static method in class dev.nm.analysis.function.FunctionOps: \(x_1 \cdot x_2\)
This operation is called inner product when used in the context of vector space.
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
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.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 - Interface in dev.nm.stat.stochasticprocess.univariate.sde.coefficients: This class represents the drift term, μ, of a univariate SDE.
drift() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.SDE: Get the drift: \(\mu(t,X_t,Z_t,...)\).
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() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt: Get the current time differential.
dt(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration: Get the i-th time increment.
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 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam
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.
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(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
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
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(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
dy() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder: Gets the first order derivative function.
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(V) - Method in interface dev.nm.graph.Graph: Gets the set of all edges associated with a vertex in this graph.
edges() - Method in class dev.nm.graph.type.SparseGraph
edges(V) - Method in class dev.nm.graph.type.SparseGraph
edges() - Method in class dev.nm.graph.type.SparseTree
edges(V) - 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
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, 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, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen: Use Eigen.Method.QR method by default.
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() - Method in class dev.nm.stat.factor.pca.PCAbyEigen: Gets the eigenvalue decomposition of the correlation (or covariance) matrix.
Eigen.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.eigen: the available methods to compute eigenvalues and eigenvectors
eigenbasis() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty: Get the eigenvectors.
EigenBoundUtils - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3: Utility methods for computing bounds of eigenvalues.
EigenCount - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3: Counts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than a given value x.
EigenCount(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCount: Creates an instance for a symmetric tridiagonal matrix T.
EigenCountInRange - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3: Finds the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given interval.
EigenCountInRange(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenCountInRange: Creates an instance for counting the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given interval.
EigenDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen: Let A be a square, diagonalizable N × N matrix with N linearly independent eigenvectors.
EigenDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition: Runs the eigen decomposition on a square matrix.
EigenDecomposition(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition: Runs the eigen decomposition on a square matrix.
EigenProperty - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen: EigenProperty is a read-only structure that contains the information about a particular eigenvalue, such as its multiplicity and eigenvectors.
eigenvalue() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty: Get the eigenvalue.
EigenvalueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds: Computes all the eigenvalues of a symmetric tridiagonal matrix.
EigenvalueByDQDS(TridiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.EigenvalueByDQDS: Computes all the eigenvalues of a symmetric tridiagonal matrix.
eigenVector() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenProperty: Get an eigenvector.
ELECTRIC_EPSILON0 - Static variable in class dev.nm.misc.PhysicalConstants: The electric permittivity or electrical constant \(\epsilon_0\) in farads per meter (F m^-1).
ELECTRON_MASS_ME - Static variable in class dev.nm.misc.PhysicalConstants: The electron rest mass \(m_e\) in kilograms (kg).
ELECTRON_VOLT_EV - Static variable in class dev.nm.misc.PhysicalConstants: The electron volt \(eV\) in joules (J).
ELEMENTARY_CHARGE_E - Static variable in class dev.nm.misc.PhysicalConstants: The elementary charge \(e\) in coulombs (C).
ElementaryFunction - Class in dev.nm.number.complex: This class contains some elementary functions for complex number, Complex.
ElementaryFunction() - Constructor for class dev.nm.number.complex.ElementaryFunction
ElementaryOperation - Class in dev.nm.algebra.linear.matrix.doubles.operation: There are three elementary row operations which are equivalent to left multiplying an elementary matrix.
ElementaryOperation(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation: Construct a transformation matrix of elementary operations.
ElementaryOperation(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation: Construct a transformation matrix of elementary operations.
ElementaryOperation(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation: Transform A by elementary operations.
elementDivide(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
elementMultiply(Matrix, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
elementOperation(Matrix, Matrix, BivariateRealFunction) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils
eliminate(GLMProblem, Matrix) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.BackwardElimination.Step
eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
eliminate(GLMProblem, Matrix) - Method in class dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue
EliminationByAIC - Class in dev.nm.stat.regression.linear.glm.modelselection: In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC.
EliminationByAIC() - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.EliminationByAIC
EliminationByZValue - Class in dev.nm.stat.regression.linear.glm.modelselection: In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level).
EliminationByZValue(double) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.EliminationByZValue: Creates an instance with the given significance level [0, 1].
Elliott2005DLM - Class in tech.nmfin.meanreversion.elliott2005: This class implements the Kalman filter model as in Elliott's paper.
Elliott2005DLM(double, double, double, double, double, RandomStandardNormalGenerator) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Constructs an Elliott's Kalman filter model.
Elliott2005DLM(double, double, double, double, double) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Constructs an Elliott's Kalman filter model.
Elliott2005DLM(Elliott2005DLM) - Constructor for class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM: Copy constructor.
ElliottOnlineFilter - Class in tech.nmfin.meanreversion.elliott2005: It is important to note that this algorithm does not guarantee that A > 0 0 < B < 1 Therefore, we need to check the outputs.
ElliottOnlineFilter(double[], Elliott2005DLM, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
ElliottOnlineFilter(double[], double, double, double, double, int) - Constructor for class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter
ElliottOnlineFilter.NoModelFitted - Exception in tech.nmfin.meanreversion.elliott2005
EmpiricalACER - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical: This class contains empirical ACER \(\hat{\epsilon_k}(\eta_i)\) values and other related statistics estimated from observations.
EmpiricalACER(double[][], double[], double, double, double[][], double[], double[]) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACER
EmpiricalACEREstimation - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical: This class estimates empirical ACER values from the given observations.
EmpiricalACEREstimation() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation: Create an instance with default values.
EmpiricalACEREstimation(int, boolean, EpsilonStatisticsCalculator, int) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation: Create an instance for counting empirical ACERs.
EmpiricalACERStatistics - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical: This class contains the computed statistics of the estimated ACERs.
EmpiricalACERStatistics(double[], double[]) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACERStatistics: Create an instance for storing the computed statistics for estimated epsilon for various barrier levels.
EmpiricalDistribution - Class in dev.nm.stat.distribution.univariate: An empirical cumulative probability distribution function is a cumulative probability distribution function that assigns probability 1/n at each of the n numbers in a sample.
EmpiricalDistribution(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution: Construct an empirical distribution from a sample.
EmpiricalDistribution(double[]) - Constructor for class dev.nm.stat.distribution.univariate.EmpiricalDistribution: Construct an empirical distribution from a sample using the default quantile type Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED.
end() - Method in class dev.nm.interval.Interval: Get the end of this interval.
ENDING_OF_TIME - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils: This represents a time after all (representable) times.
ENDING_OF_TIME_LONG - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils: This represents a time after all (representable) times, in long representation.
energy(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement
entropy() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution
entropy() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution: Gets the entropy of this distribution.
entropy() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution: Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.FDistribution: Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution: Deprecated.
Not supported yet.
entropy() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution
entropy() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution: Gets the entropy of this distribution.
entropy() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.TDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution
entropy() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution
entropy() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD
entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD
entropy() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution
entropy() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution
entropy() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution
entropy() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution: Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution: Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution: Deprecated.
entropy() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution: Deprecated.
entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution: Deprecated.
entropy() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution: Deprecated.
Entry(MatrixCoordinate, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry: Construct a sparse entry in a sparse matrix.
Entry(int, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
Entry(T, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry: Construct an instance of TimeSeries.Entry.
Entry(int, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateIntTimeTimeSeries.Entry: Construct an instance of Entry.
Entry(double, Vector) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.MultivariateRealization.Entry: Construct an instance of TimeSeries.Entry.
Entry(int, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.IntTimeTimeSeries.Entry: Construct an instance of TimeSeries.Entry.
Entry(double, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.Realization.Entry: Construct an instance of TimeSeries.Entry.
Entry(T, double) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry: Construct an instance of Entry.
EPSILON - Static variable in class dev.nm.misc.Constants: the default epsilon used in this library
epsilon - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1
epsilon() - Method in interface dev.nm.solver.multivariate.constrained.integer.IPProblem: Get the threshold to check whether a variable is an integer.
epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1
epsilon() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1
epsilon - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer
epsilon - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer: a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer: the convergence tolerance
epsilon1 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
epsilon2 - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer
EpsilonStatisticsCalculator - Class in dev.nm.stat.evt.evd.univariate.fitting.acer.empirical: Compute statistics: mean, confidence interval of estimated ACER values \(\epsilon_k(\eta_i)\).
EpsilonStatisticsCalculator(boolean, double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EpsilonStatisticsCalculator: Create an instance with the weighting option and confidence interval.
equal(double, double, double) - Static method in class dev.nm.number.DoubleUtils: Check if two doubles are close enough, hence equal.
equal(double[], double[], double) - Static method in class dev.nm.number.DoubleUtils: Check if two double arrays are close enough, hence equal, entry-by-entry.
equal(double[][], double[][], double) - Static method in class dev.nm.number.DoubleUtils: Check if two 2D arrays, double[][], are close enough, hence equal, entry-by-entry.
equal(int[], int[]) - Static method in class dev.nm.number.DoubleUtils: Check if two int arrays, int[], are equal, entry-by-entry.
equal(Number, Number, double) - Static method in class dev.nm.number.NumberUtils: Check the equality of two Numbers, up to a precision.
EqualityConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint: The domain of an optimization problem may be restricted by equality constraints.
EqualityConstraints(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints: Construct the equality constraints for a dual SDP problem, \(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).
EqualityConstraints(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints: Constructs the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ..., q\).
EquallySpacedVariable(double, double) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid.EquallySpacedVariable: Create a new instance which specifies the position of the first element and the spacing along a dimension as the given values.
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry
equals(SparseMatrix, SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils: Checks if two SparseMatrixs are equal.
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry
equals(Object) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix
equals(Object) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix
equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector
equals(Object) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector
equals(Object) - Method in class dev.nm.analysis.function.polynomial.Polynomial
equals(Object) - Method in class dev.nm.analysis.function.tuple.Pair
equals(Graph<V, ?>, Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils: Check if two graphs are equal in terms of node values and edges.
equals(Object) - Method in class dev.nm.interval.Interval
equals(Object) - Method in class dev.nm.interval.Intervals
equals(Object) - Method in class dev.nm.misc.datastructure.FlexibleTable
equals(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet
equals(Object) - Method in class dev.nm.misc.datastructure.SortableArray
equals(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils: Check if two BigDecimals are equal up to a precision.
equals(Object) - Method in class dev.nm.number.complex.Complex
equals(Object) - Method in class dev.nm.number.Real
equals(Object) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries
equals(Object) - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry
Erf - Class in dev.nm.analysis.function.special.gaussian: The Error function is defined as: \[ \operatorname{erf}(x) = \frac{2}{\sqrt{\pi}}\int_{0}^x e^{-t^2} dt \]
Erf() - Constructor for class dev.nm.analysis.function.special.gaussian.Erf
Erfc - Class in dev.nm.analysis.function.special.gaussian: This complementary Error function is defined as: \[ \operatorname{erfc}(x) = 1-\operatorname{erf}(x) = \frac{2}{\sqrt{\pi}} \int_x^{\infty} e^{-t^2}\,dt \]
Erfc() - Constructor for class dev.nm.analysis.function.special.gaussian.Erfc
ErfInverse - Class in dev.nm.analysis.function.special.gaussian: The inverse of the Error function is defined as: \[ \operatorname{erf}^{-1}(x) \]
ErfInverse() - Constructor for class dev.nm.analysis.function.special.gaussian.ErfInverse
ErgodicHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid: A simple decorator which will randomly vary dt between each sample.
ErgodicHybridMCMC(double, UnivariateRealFunction, AbstractHybridMCMC) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC: Constructs a new instance where dt is given as a function.
ErgodicHybridMCMC(double, double, RandomLongGenerator, AbstractHybridMCMC) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC: Constructs a new instance where dt is uniformly drawn from a given range.
error(T) - Method in interface dev.nm.solver.multivariate.minmax.MinMaxProblem: e(x, ω) is the error function, or the minmax objective, for a given ω.
EST - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils: EST
estimate(Matrix) - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016
estimate(double[][]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.EmpiricalACEREstimation: Estimate epsilon (or ACERs) from the given observations (each row is for one period).
estimate(int) - Method in interface dev.nm.stat.random.MeanEstimator: Gets an estimator.
estimate(int) - Method in class dev.nm.stat.random.variancereduction.AntitheticVariates
estimate(int) - Method in class dev.nm.stat.random.variancereduction.CommonRandomNumbers
estimate(int) - Method in class dev.nm.stat.random.variancereduction.ControlVariates
estimate(int) - Method in class dev.nm.stat.random.variancereduction.ImportanceSampling
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1
estimate(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2
estimate(Matrix) - Method in class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf
estimate(Matrix) - Method in interface tech.nmfin.returns.moments.ReturnsMoments.Estimator: Estimates the moments from a given returns matrix.
EstimateByLogLikelihood - Class in dev.nm.stat.evt.evd.univariate.fitting: Result from maximum likelihood fitting algorithm, which contains: the log-likelihood function, the fitted parameters for the target model, the variance-covariance matrix, the standard errors, the confidence intervals.
EstimateByLogLikelihood(Vector, RealScalarFunction) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood
estimated_lambda - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result: estimated lambda
estimatedPopulationEigenvalues(Vector) - Method in class dev.nm.stat.covariance.nlshrink.TauEstimator: Estimates population eigenvalues from given sample eigenvalues.
estimateForMultiPeriods(double[][]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting: Estimate for multiple periods.
estimateForOnePeriod(double[]) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERByCounting: Estimate for a single period.
Estimator - Interface in dev.nm.stat.random
eta() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution
EULER_MASCHERONI - Static variable in class dev.nm.misc.Constants: the Euler-Mascheroni constant
EulerMethod - Class in dev.nm.analysis.differentialequation.ode.ivp.solver: The Euler method is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.
EulerMethod(double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod: Constructs an Euler's method instance, with the given step size.
EulerMethod(int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.EulerMethod: Constructs an Euler's method instance, with the given number of steps.
EulerSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete: The Euler scheme is the first order approximation of an SDE.
EulerSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.EulerSDE: Discretize a continuous-time SDE using the Euler scheme.
evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.LinearInterpolator
evaluate(double) - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable
evaluate(double, Vector) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.problem.DerivativeFunction: Computes the derivative at the given point, x.
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.GradientFunction
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.HessianFunction
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.JacobianFunction
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference: Evaluate numerically the partial derivative of f at point x.
evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference: Evaluate numerically the partial derivative of f at point x with step size h.
evaluate(Vector) - Method in class dev.nm.analysis.differentiation.Ridders: Evaluate the function f at x, where x is from the domain.
evaluate(double) - Method in class dev.nm.analysis.differentiation.Ridders: Evaluate f'(x), where f is a UnivariateRealFunction.
evaluate(Vector, double) - Method in class dev.nm.analysis.differentiation.Ridders: Evaluate numerically the derivative of f at point x, f'(x), with step size h.
evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.DBeta: Evaluate \({\partial \over \partial x} \mathrm{B}(x, y)\).
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DBetaRegularized: Evaluate \({\partial \over \partial x} \mathrm{B_x}(p, q)\).
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DErf
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.Dfdx
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGamma
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.DGaussian
evaluate(double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference
evaluate(double, double) - Method in class dev.nm.analysis.differentiation.univariate.FiniteDifference: Evaluate numerically the derivative of f at point x, f'(x), with step size h.
evaluate(D) - Method in interface dev.nm.analysis.function.Function: Evaluate the function f at x, where x is from the domain.
evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toConstantMatrix
evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R1toMatrix
evaluate(double) - Method in class dev.nm.analysis.function.matrix.R1toMatrix: Evaluate f(x) = A.
evaluate(Vector) - Method in class dev.nm.analysis.function.matrix.R2toMatrix
evaluate(double, double) - Method in class dev.nm.analysis.function.matrix.R2toMatrix: Evaluate f(x₁, x₂) = A.
evaluate(Number) - Method in class dev.nm.analysis.function.polynomial.Polynomial: Evaluate this polynomial at x.
evaluate(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial: Evaluate this polynomial at x.
evaluate(Complex) - Method in class dev.nm.analysis.function.polynomial.Polynomial: Evaluate this polynomial at x.
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.AbstractTrivariateRealFunction
evaluate(double, double) - Method in interface dev.nm.analysis.function.rn2r1.BivariateRealFunction: Evaluate y = f(x₁,x₂).
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.R1Projection
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.RealScalarSubFunction: Evaluate the function f at x.
evaluate(double, double, double) - Method in interface dev.nm.analysis.function.rn2r1.TrivariateRealFunction: Evaluate y = f(x₁,x₂,x₃).
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2r1.univariate.AbstractUnivariateRealFunction
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction: Evaluate y = f(x).
evaluate(BigDecimal) - Method in class dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction: Evaluate z.
evaluate(double) - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction
evaluate(double) - Method in interface dev.nm.analysis.function.rn2r1.univariate.UnivariateRealFunction: Evaluate y = f(x).
evaluate(double) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.AbstractR1RnFunction
evaluate(Vector) - Method in class dev.nm.analysis.function.rn2rm.RealVectorSubFunction
evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.Beta: Evaluate B(x,y).
evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularized: Evaluate I_x(p,q).
evaluate(double) - Method in class dev.nm.analysis.function.special.beta.BetaRegularizedInverse: Evaluate \(I^{-1}_{(p,q)}(u)\).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.beta.LogBeta: Compute log(B(x,y)).
evaluate(Vector) - Method in class dev.nm.analysis.function.special.beta.MultinomialBetaFunction
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Digamma
evaluate(double) - Method in interface dev.nm.analysis.function.special.gamma.Gamma: Evaluate \(\Gamma(z) = \int_0^\infty e^{-t} t^{z-1} dt\).
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaGergoNemes
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczos
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.GammaLanczosQuick
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaLowerIncomplete: Evaluate \(\gamma(s,x)\).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedP: Evaluate P(s,x).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedPInverse: Evaluate x = P^-1(s,u).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaRegularizedQ: Evaluate Q(s,x).
evaluate(double, double) - Method in class dev.nm.analysis.function.special.gamma.GammaUpperIncomplete: Compute Γ(s,x).
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.LogGamma: Evaluate the log of the Gamma function in the positive real domain.
evaluate(double) - Method in class dev.nm.analysis.function.special.gamma.Trigamma
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalHastings
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalInverse
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.CumulativeNormalMarsaglia
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erf
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Erfc
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.ErfInverse
evaluate(double) - Method in class dev.nm.analysis.function.special.gaussian.Gaussian
evaluate(double) - Method in interface dev.nm.analysis.function.special.gaussian.StandardCumulativeNormal: Evaluate \(F(x;\,\mu,\sigma^2)\).
evaluate(Vector) - Method in class dev.nm.analysis.function.special.Rastrigin
evaluate(double) - Method in interface dev.nm.analysis.sequence.Summation.Term: Evaluate the term for an index.
evaluate(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils: Evaluates the constraints.
evaluate(Matrix) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp: Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction: Computes the final objective function value.
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.AbsoluteErrorPenalty
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.CourantPenalty
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.FletcherPenalty
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties
evaluate(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty
evaluate(Realization[]) - Method in interface dev.nm.stat.cointegration.JohansenAsymptoticDistribution.F: F(B).
evaluate() - Method in class dev.nm.stat.covariance.nlshrink.quest.QuEST
evaluate(X) - Method in interface dev.nm.stat.distribution.discrete.ProbabilityMassFunction: Compute the probability mass for a discrete realization x.
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction: Compute the epsilon value.
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERInverseFunction
evaluate(double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERLogFunction: Compute the log-scale epsilon value at the given barrier level.
evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnLevel: Compute the return level from the given return period.
evaluate(double) - Method in class dev.nm.stat.evt.function.ReturnPeriod
evaluate(Vector, Vector) - Method in interface dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings.ProposalDensityFunction: Evaluates the density at the given points.
evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.GaussianProposalFunction
evaluate(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantDriftVector
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix: Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma
evaluate(MultivariateFt) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedRealFunction: Evaluate this function, f, at time t.
evaluate(MultivariateFt) - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction: Evaluate this function, f, at time t.
evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Bt
evaluate(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction: Compute the function value at the i-th time point, \(f(\mathfrak{F_{t_i}})\).
evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction
evaluate(Ft) - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.FtAdaptedFunction: Evaluate this function, f, at time t.
evaluate(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction: Evaluate this function, f, based on only the current value of the stochastic process.
evaluate(Ft) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation: Get the i-th auto-correlation matrix.
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance: Get the i-th auto-covariance matrix.
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation: Compute the auto-correlation for lag k.
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance: Compute the auto-covariance for lag k.
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation
evaluate(int) - Method in class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation: Compute the partial auto-correlation for lag k.
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation: Get the i-th auto-correlation.
evaluate(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance
evaluate(double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance: Gets the i-th auto-covariance.
evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta
evaluate(double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMomentsEstimator
evaluate(double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction
evaluate(double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1: Note: x here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2: Note: x here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.
evaluate(Vector) - Method in class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure
EvaluationException(String) - Constructor for exception dev.nm.analysis.function.Function.EvaluationException: Constructs an EvaluationException with the specified detail message.
EvenlySpacedGrid - Class in dev.nm.stat.stochasticprocess.timegrid: This is an evenly spaced time grid.
EvenlySpacedGrid(double, double, int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid: Construct an evenly spaced time grid.
ExceptionUtils - Class in dev.nm.misc: Exception-related utility functions.
execute(Runnable) - Method in class dev.nm.misc.parallel.Mutex: The runnable is executed under synchronization of this Mutex instance.
executeAll(List<? extends Callable<T>>) - Method in class dev.nm.misc.parallel.ParallelExecutor: Executes a list of Callable tasks, and returns a list of results in the same sequential order as tasks.
executeAll(Callable<T>...) - Method in class dev.nm.misc.parallel.ParallelExecutor: Executes an arbitrary number of Callable tasks, and returns a list of results in the same order.
executeAny(List<? extends Callable<T>>) - Method in class dev.nm.misc.parallel.ParallelExecutor: Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
executeAny(Callable<T>...) - Method in class dev.nm.misc.parallel.ParallelExecutor: Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
exp(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation: Computes the exponential of a vector, element-by-element.
exp(double) - Static method in class dev.nm.number.big.BigDecimalUtils: Compute e^x.
exp(double, int) - Static method in class dev.nm.number.big.BigDecimalUtils: Compute e^x.
exp(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils: Compute e^x.
exp(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils: Compute e^x.
exp(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction: Exponential of a complex number.
exp(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath: Get the exponentials of values.
ExpectationAtEndTime - Class in dev.nm.stat.stochasticprocess.univariate.random: This class computes the expectation (mean) and the variance of a stochastic process, by Monte Carlo simulation, at the end of an interval: \(E(X_T)\).
ExpectationAtEndTime(RandomRealizationGenerator, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime: Compute the expectation of a random process at the end time.
ExpectationAtEndTime(RandomProcess, int, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime: Compute the expectation of a random process at the end time.
ExpectationAtEndTime(SDE, double, double, int, double, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime: Compute the expectation of a stochastic SDE at the end time.
expectedPnL() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction: Gets the expected PnL when playing the H strategy in mean-reverting model.
ExplicitCentralDifference1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1: This explicit central difference method is a numerical technique for solving the one-dimensional wave equation by the following explicit three-point central difference equation.
ExplicitCentralDifference1D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D
ExplicitCentralDifference2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2: This explicit central difference method is a numerical technique for solving the two-dimensional wave equation by the following explicit three-point central difference equation.
ExplicitCentralDifference2D() - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D
ExplicitImplicitModelPCA - Class in dev.nm.stat.factor.implicitmodelpca: Given a time series of vectored observations, we decompose them into a reduced dimension of linear sum of both explicit/specified and implicit factors.
ExplicitImplicitModelPCA(Matrix, Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
ExplicitImplicitModelPCA(Matrix, Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA
ExplicitImplicitModelPCA.Result - Class in dev.nm.stat.factor.implicitmodelpca
expm1(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath: Get the exponential-minus-one (e^x - 1) of values.
exponent() - Method in class dev.nm.number.ScientificNotation: Get the exponent.
Exponential - Class in dev.nm.analysis.integration.univariate.riemann.substitution: This transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially.
Exponential(double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential: Construct an Exponential substitution rule.
ExponentialDistribution - Class in dev.nm.stat.distribution.univariate: The exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate.
ExponentialDistribution() - Constructor for class dev.nm.stat.distribution.univariate.ExponentialDistribution: Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
ExponentialDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.ExponentialDistribution: Construct an exponential distribution.
ExponentialFamily - Class in dev.nm.stat.distribution.univariate.exponentialfamily: The exponential family is an important class of probability distributions sharing this particular form.
ExponentialFamily(UnivariateRealFunction, RealVectorFunction, AbstractR1RnFunction, RealScalarFunction) - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.ExponentialFamily: Construct a factory to construct probability distribution in the exponential family of this form.
ExponentialMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution: The HMM states use the Exponential distribution to model the observations.
ExponentialMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution: Constructs an Exponential distribution for each state in the HMM model.
ExpTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction: Logarithmic decay, where \(T_k = T_0 * 0.95^k\).
ExpTemperatureFunction(double) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction: Constructs a new instance with an initial temperature.
exsec(double) - Static method in class dev.nm.geometry.TrigMath: Returns the exsecant of an angle.
extractPeaks(double[]) - Static method in class dev.nm.stat.evt.evd.univariate.fitting.acer.empirical.ACERUtils: Extract peaks (values which are preceded and followed by values smaller than itself).
extrema() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction
ExtremalGeneralizedEigenvalueByGreedySearch - Class in tech.nmfin.meanreversion.daspremont2008: Solves \[ \min_x \frac{x'Ax}{x'Bx} \\ \textup{s.t.,} \mathbf{Card}(x) \leqslant k, \left \| x \right \| = 1 \]
ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch: Constructs the problem described in Section 3.2, d'Aspremont (2008), changed to a minimization problem.
ExtremalGeneralizedEigenvalueByGreedySearch(Matrix, Matrix, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueByGreedySearch: Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP - Class in tech.nmfin.meanreversion.daspremont2008: Solves the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP: Constructs the problem described in Section 3.2, d'Aspremont (2008), changed to a minimization problem.
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP: Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueBySDP(SymmetricMatrix, SymmetricMatrix, int, double, double, double, boolean) - Constructor for class tech.nmfin.meanreversion.daspremont2008.ExtremalGeneralizedEigenvalueBySDP: Constructs the problem described in Section 3.2, d'Aspremont (2008).
ExtremalGeneralizedEigenvalueSolver - Interface in tech.nmfin.meanreversion.daspremont2008
ExtremalIndexByClusterSizeReciprocal - Class in dev.nm.stat.evt.exi: This class estimates the extremal index by the reciprocal of the average cluster size.
ExtremalIndexByClusterSizeReciprocal(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal
ExtremalIndexByClusterSizeReciprocal(double, int) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal: Create an instance with the given threshold and clustering interval length.
ExtremalIndexByFerroSeegers - Class in dev.nm.stat.evt.exi: This class estimates the extremal index from observations by the algorithm proposed by Ferro and Seegers.
ExtremalIndexByFerroSeegers(double) - Constructor for class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers: Create an instance with the given threshold.
ExtremalIndexEstimation - Interface in dev.nm.stat.evt.exi: The extremal index \(\theta \in [0,1]\) characterizes the degree of local dependence in the extremes of a stationary time series.
ExtremeValueMC - Class in dev.nm.stat.evt.markovchain: Simulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions.
ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType) - Constructor for class dev.nm.stat.evt.markovchain.ExtremeValueMC: Create an instance with a given bivariate distribution that defines the dependence structure between two consecutive simulated values, and uses UniformRNG for random number generation.
ExtremeValueMC(BivariateEVD, ExtremeValueMC.MarginalDistributionType, RandomNumberGenerator) - Constructor for class dev.nm.stat.evt.markovchain.ExtremeValueMC: Create an instance with a given bivariate distribution that defines the dependence structure between two consecutive simulated values, and a uniform random number generator.
ExtremeValueMC.MarginalDistributionType - Enum in dev.nm.stat.evt.markovchain: Types of marginal distribution of each simulated value.
Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel
Ey(Vector) - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent
Ey(Vector) - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP
Ey(Vector) - Method in interface dev.nm.stat.regression.linear.LinearModel: Computes the expectation \(E(y(x))\) given an input.
Ey(Vector) - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression: Calculates the probability of occurrence (y = 1).
Ey(Vector) - Method in class dev.nm.stat.regression.linear.ols.OLSRegression
Ey(Vector, Vector, boolean) - Static method in class dev.nm.stat.regression.linear.ols.OLSRegression

F

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

G

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

H

H() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition

Gets the H matrix.

H() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Get the Householder matrix H = I - 2 * v * v'.

H() - Method in interface dev.nm.analysis.differentiation.differentiability.C2

Get the Hessian matrix function, H, of a real valued function f.

h() - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator

Get the discretization size for the current iteration.

h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes

h() - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson

H() - Method in class dev.nm.solver.problem.C2OptimProblemImpl

H(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Gets H(t), the covariance matrix of u_t.

H(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Get H(t), the variance of u_t.

H(LeapFrogging.DynamicsState, RealScalarFunction, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC

Evaluates a system's total energy at a given state.

H - Variable in class tech.nmfin.infantino2010.Infantino2010PCA

H() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal

H() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

Hadi() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics

Hadi's influence measure.

HalleyRoot - Class in dev.nm.analysis.root.univariate

Halley's method is an iterative root finding method for a univariate function with a continuous second derivative, i.e., a C² function.

HalleyRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.HalleyRoot

Construct an instance of Halley's root finding algorithm.

HarveyGodfrey - Class in dev.nm.stat.test.regression.linear.heteroskedasticity

The Harvey-Godfrey test tests for conditional heteroskedasticity.

HarveyGodfrey(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.HarveyGodfrey

Perform the Harvey-Godfrey test to test for heteroskedasticity in a linear regression model.

hasDuplicate(double[], double) - Static method in class dev.nm.number.DoubleUtils

Check if a double array contains any duplicates.

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry

hashCode() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

hashCode() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

hashCode() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

hashCode() - Method in class dev.nm.analysis.function.polynomial.Polynomial

hashCode() - Method in class dev.nm.analysis.function.tuple.Pair

hashCode() - Method in class dev.nm.interval.Interval

hashCode() - Method in class dev.nm.interval.Intervals

hashCode() - Method in class dev.nm.misc.datastructure.FlexibleTable

hashCode() - Method in class dev.nm.misc.datastructure.IdentityHashSet

hashCode() - Method in class dev.nm.misc.datastructure.SortableArray

hashCode() - Method in class dev.nm.number.complex.Complex

hashCode() - Method in class dev.nm.number.Real

hashCode() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries.Entry

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries.Entry

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

hashCode() - Method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries.Entry

hasNext() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator

hasZero(double[], double) - Static method in class dev.nm.number.DoubleUtils

Check if a double array has any 0.

hav(double) - Static method in class dev.nm.geometry.TrigMath

Returns the haversed sine or haversine of an angle.

HConstruction<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility

A construction of extreme and trade points based on H discretization, ignoring changes smaller than H.

head() - Method in interface dev.nm.graph.Arc

head() - Method in class dev.nm.graph.type.SimpleArc

header() - Method in interface dev.nm.stat.regression.linear.panel.PanelData.Transformation

Gets the name of the column to apply the transformation to.

HeatEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation

A one-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.

HeatEquation1D(double, double, double, UnivariateRealFunction, double, UnivariateRealFunction, double, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D

Constructs a heat equation problem.

HeatEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2

A two-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the following form.

HeatEquation2D(double, double, double, double, BivariateRealFunction, TrivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D

Constructs a two-dimensional heat equation problem.

height() - Method in interface dev.nm.graph.RootedTree

Gets the maximum depth in this tree.

height() - Method in class dev.nm.graph.type.SparseTree

height() - Method in class dev.nm.graph.type.VertexTree

HermitePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

A Hermite polynomial is defined by the recurrence relation below.

HermitePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermitePolynomials

HermiteRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

HermiteRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule

Create a Hermite rule of the given order.

Hessenberg - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

An upper Hessenberg matrix is a square matrix which has zero entries below the first sub-diagonal.

Hessenberg(DeflationCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

Construct a Hessenberg utility class.

Hessenberg() - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

Construct a Hessenberg utility class with the default deflation criterion.

HessenbergDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.

HessenbergDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition

Runs the Hessenberg decomposition for a square matrix.

HessenbergDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

Given a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix.

HessenbergDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch

HessenbergDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDeflationSearch

Hessian - Class in dev.nm.analysis.differentiation.multivariate

The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.

Hessian(RealScalarFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Hessian

Construct the Hessian matrix for a multivariate function f at point x.

Hessian() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction

HessianFunction - Class in dev.nm.analysis.differentiation.multivariate

The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x.

HessianFunction(RealScalarFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.HessianFunction

Construct the Hessian function of a real scalar function f.

Heteroskedasticity - Class in dev.nm.stat.test.regression.linear.heteroskedasticity

A heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors).

Heteroskedasticity(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity

Construct a heteroskedasticity test.

hHat() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the projection matrix, H-hat.

HiddenMarkovModel - Class in dev.nm.stat.hmm

HiddenMarkovModel(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel

HiddenMarkovModel(HMMRNG) - Constructor for class dev.nm.stat.hmm.HiddenMarkovModel

HilbertMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype

A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)

HilbertMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix

Constructs a Hilbert matrix.

HilbertSpace<H,F extends Field<F> & Comparable<F>> - Interface in dev.nm.algebra.structure

A Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured.

HmmInnovation - Class in dev.nm.stat.hmm

An HMM innovation consists of a state and an observation in the state.

HmmInnovation(int, double) - Constructor for class dev.nm.stat.hmm.HmmInnovation

Construct an HMM innovation.

HMMRNG - Class in dev.nm.stat.hmm

In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible.

HMMRNG(Vector, Matrix, RandomNumberGenerator[]) - Constructor for class dev.nm.stat.hmm.HMMRNG

Constructs a hidden Markov model.

HMMRNG(HMMRNG) - Constructor for class dev.nm.stat.hmm.HMMRNG

Copy constructor.

holdingTime(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Gets a suggested holding time based on an OU process.

holdingTimeByThreshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Gets a suggested holding time based on an OU process.

HomogeneousPathFollowingMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing

This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.

HomogeneousPathFollowingMinimizer(double, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer

Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.

HomogeneousPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer

Constructs a Homogeneous Self-Dual Path-Following minimizer to solve semi-definite programming problems.

HomogeneousPathFollowingMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing

This is the solution to a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm.

HornerScheme - Class in dev.nm.analysis.function.polynomial

Horner scheme is an algorithm for the efficient evaluation of polynomials in monomial form.

HornerScheme(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.HornerScheme

Evaluate a polynomial at x.

Householder4SubVector - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

Faster implementation of Householder reflection for sub-vectors at a given index.

Householder4SubVector(int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

Householder4SubVector(int, int, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

Householder4SubVector(int, HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

Householder4SubVector(int, Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

Householder4ZeroGenerator - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

Faster implementation of Householder reflection for zero generator vector.

Householder4ZeroGenerator(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator

HouseholderContext - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

This is the context information about a Householder transformation.

HouseholderContext(Vector, double, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext

Constructs a Householder context information.

HouseholderInPlace - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

Maintains the matrix to be transformed by a sequence of Householder reflections.

HouseholderInPlace(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Creates an instance that transforms the given matrix A.

HouseholderInPlace(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Creates an instance that transforms the given matrix A.

HouseholderInPlace(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Creates an instance that transforms an identity matrix with the given dimension.

HouseholderInPlace.Householder - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

HouseholderQR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr

Successive Householder reflections gradually transform a matrix A to the upper triangular form.

HouseholderQR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

Runs the Householder reflection process to orthogonalize a matrix.

HouseholderQR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

Runs the Householder reflection process to orthogonalize a matrix.

HouseholderReflection - Class in dev.nm.algebra.linear.matrix.doubles.operation.householder

A Householder transformation in the 3-dimensional space is the reflection of a vector in the plane.

HouseholderReflection(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Construct a Householder matrix from the vector that defines the hyperplane orthogonal to the vector.

HouseholderReflection(HouseholderContext) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

HouseholderReflection(Vector, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Hp - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing

This is the symmetrization operator as defined in equation (6) in the reference.

Hp(Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp

Constructs a symmetrization operator.

Hp() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.Hp

Constructs the symmetrization operator using an identity matrix.

HuangImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer.HuangImpl

HuangMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

Huang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas.

HuangMinimizer(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer

Construct a multivariate minimizer using Huang's method.

HuangMinimizer.HuangImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

an implementation of Huang's formula.

HybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid

This class implements a hybrid MCMC algorithm.

HybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC

Constructs a new instance with the given parameters.

HybridMCMCProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction

HybridMCMCProposalFunction(Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.HybridMCMCProposalFunction

Constructs a hybrid MC proposal function.

HyperEdge<V> - Interface in dev.nm.graph

A hyper-edge connects a set of vertices of any size.

HypersphereRVG - Class in dev.nm.stat.random.rng.multivariate

Generates uniformly distributed points on a unit hypersphere.

HypersphereRVG(int, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG

Constructs a hypersphere RVG to generate random uniform points.

HypersphereRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.HypersphereRVG

Constructs a hypersphere RVG to generate random uniform points.

HypothesisTest - Class in dev.nm.stat.test

A statistical hypothesis test is a method of making decisions using experimental data.

HypothesisTest(double[]...) - Constructor for class dev.nm.stat.test.HypothesisTest

Construct an instance of HypothesisTest from the samples.

I

i() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Gets the value of i.

i - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate

the row index

I - Static variable in class dev.nm.number.complex.Complex

a number representing 0.0 + 1.0i, the square root of -1

I - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

identity(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a new identity matrix.

IdentityHashSet<T> - Class in dev.nm.misc.datastructure

This class implements the Set interface with a hash table, using reference-equality in place of object-equality when comparing keys and values.

IdentityHashSet() - Constructor for class dev.nm.misc.datastructure.IdentityHashSet

Construct an empty IdentityHashSet.

IdentityHashSet(Collection<T>) - Constructor for class dev.nm.misc.datastructure.IdentityHashSet

Construct an IdentityHashSet with a collection of items.

IdentityPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

This identity preconditioner is used when no preconditioning is applied.

IdentityPreconditioner() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner

ifelse(double[], DoubleUtils.ifelse) - Static method in class dev.nm.number.DoubleUtils

Return a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is true or false.

IID - Class in dev.nm.stat.random.rng.multivariate

An i.i.d.

IID(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.IID

Construct a rvg that outputs vectors that have i.i.d.

ILPBranchAndBoundMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.bb

This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems.

ILPBranchAndBoundMinimizer(ILPBranchAndBoundMinimizer.ActiveListFactory) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer

Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.

ILPBranchAndBoundMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer

Construct a Branch-and-Bound minimizer to solve Integer Linear Programming problems.

ILPBranchAndBoundMinimizer.ActiveListFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.bb

This factory constructs a new instance of ActiveList for each Integer Linear Programming problem.

ILPNode - Class in dev.nm.solver.multivariate.constrained.integer.linear.bb

This is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem.

ILPNode(ILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode

Construct a BB node and associate it with an ILP problem.

ILPProblem - Interface in dev.nm.solver.multivariate.constrained.integer.linear.problem

A linear program in real variables is said to be integral if it has at least one optimal solution which is integral.

ILPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.integer.linear.problem

This implementation is an ILP problem, in which the variables can be real or integral.

ILPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, int[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

Construct an ILP problem, in which the variables can be real or integral.

imaginary() - Method in class dev.nm.number.complex.Complex

Get the imaginary part of this complex number.

ImmutableMatrix - Class in dev.nm.algebra.linear.matrix.doubles

This is a read-only view of a Matrix instance.

ImmutableMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

Construct a read-only version of a matrix.

ImmutableVector - Class in dev.nm.algebra.linear.vector.doubles

This is a read-only view of a Vector instance.

ImmutableVector(Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.ImmutableVector

Construct a read-only version of a vector.

ImplicitModelPCA - Class in dev.nm.stat.factor.implicitmodelpca

Given a (de-meaned) time series of vectored observations, we decompose them into a reduced dimension of linear sum of implicit factors.

ImplicitModelPCA(Matrix, int) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA

Constructs an implicit-model that will have K implicit factors.

ImplicitModelPCA(Matrix) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA

Constructs an implicit-model that will have one and only one implicit factors.

ImplicitModelPCA(Matrix, double) - Constructor for class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA

Constructs an implicit-model that will have the number of implicit factors such that the variance explained is bigger than a threshold

ImplicitModelPCA.Result - Class in dev.nm.stat.factor.implicitmodelpca

the regression results

ImportanceSampling - Class in dev.nm.stat.random.variancereduction

Importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than the distribution of interest.

ImportanceSampling(UnivariateRealFunction, UnivariateRealFunction, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.variancereduction.ImportanceSampling

inactiveSize() - Method in class dev.nm.misc.algorithm.ActiveSet

Get the number of inactive indices.

incomingArcs(V) - Method in interface dev.nm.graph.DiGraph

Gets the set of all incoming arcs associated with a vertex in this graph.

incomingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph

incomingArcs(V) - Method in class dev.nm.graph.type.SparseTree

incomingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

IndependentCoVAR - Class in tech.nmfin.meanreversion.daspremont2008

This algorithm finds the independent variables based on the covariance matrix.

IndependentCoVAR(Matrix, double) - Constructor for class tech.nmfin.meanreversion.daspremont2008.IndependentCoVAR

Runs the algorithm with the given covariance matrix.

index() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry

Gets the index of this entry in the sparse vector, counting from 1.

index - Variable in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound

the index to the variable, counting from 1.

index - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label

the index of a variable, an inequality, an equality, etc., counting from 1

index - Variable in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain

the index of an integral variable

Infantino2010PCA - Class in tech.nmfin.infantino2010

The objective is to predict the next H-period accumulated returns from the past H-period dimensionally reduced returns.

Infantino2010PCA(int, int, int, int, boolean) - Constructor for class tech.nmfin.infantino2010.Infantino2010PCA

Infantino2010PCA.Signal - Class in tech.nmfin.infantino2010

Infantino2010Regime - Class in tech.nmfin.infantino2010

Detects the current regime (mean reversion or momentum) by cross-sectional volatility.

Infantino2010Regime(int) - Constructor for class tech.nmfin.infantino2010.Infantino2010Regime

Infantino2010Regime.Regime - Enum in tech.nmfin.infantino2010

informationCriteria() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression

Gets the model selection criteria.

init(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

Initializes the algorithm states with initial \(x_{min}\) and \(f_{min}\) before iterations.

init(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

initialCondition() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D

Initializes the grid with the initial conditions.

initialReturns - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2

initials - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

InitialsFactory - Interface in dev.nm.solver.multivariate.initialization

Some optimization algorithms, e.g., Nelder-Mead, Differential-Evolution, require a set of initial points to work with.

initialSigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2

innerProduct(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

innerProduct(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

InnerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation

The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.

InnerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct

Compute the inner product of two matrices.

innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

innerProduct(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

innerProduct(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the inner or dot product of two vectors.

innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

innerProduct(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

innerProduct(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Inner product in the Euclidean space is the dot product.

innerProduct(H) - Method in interface dev.nm.algebra.structure.HilbertSpace

<⋅,⋅> : H × H → F
Inner product formalizes the geometrical notions such as the length of a vector and the angle between two vectors.

InnovationsAlgorithm - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess

The innovations algorithm is an efficient way to obtain a one step least square linear predictor for a univariate linear time series with known auto-covariance and these properties (not limited to ARMA processes): {x_t} can be non-stationary. E(x_t) = 0 for all t. This class implements the part of the innovations algorithm that computes the prediction error variances, v and prediction coefficients θ.

InnovationsAlgorithm(int, AutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm

Constructs an instance of InnovationsAlgorithm for a univariate time series with known auto-covariance structure.

intArray2doubleArray(int...) - Static method in class dev.nm.number.DoubleUtils

Convert an int array to a double array.

intArray2List(int[]) - Static method in class dev.nm.number.DoubleUtils

Convert an int array to a list.

IntegerDomain(int, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain

Construct the integral domain for an integral variable.

IntegerDomain(int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain

Construct the integral domain for an integral variable.

IntegerDomain(int, int, int, int) - Constructor for class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPProblem.IntegerDomain

Construct the integral domain for an integral variable.

Integral - Class in dev.nm.stat.stochasticprocess.univariate.integration

The class represents an integral of a function, in the Lebesgue sense.

Integral(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.Integral

Construct an integral from an integrand.

IntegralConstrainedCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained

This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem.

IntegralConstrainedCellFactory(DEOptimCellFactory, IntegralConstrainedCellFactory.IntegerConstraint) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory

Construct an instance of IntegralConstrainedCellFactory.

IntegralConstrainedCellFactory.AllIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained

This integral constraint makes all variables in the objective function integral variables.

IntegralConstrainedCellFactory.IntegerConstraint - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained

The integral constraints are defined by implementing this interface.

IntegralConstrainedCellFactory.SomeIntegers - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained

This integral constraint makes some variables in the objective function integral variables.

IntegralDB - Class in dev.nm.stat.stochasticprocess.univariate.integration

This class evaluates the following class of integrals.

IntegralDB(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDB

Construct an integral for f with respect to dB.

IntegralDt - Class in dev.nm.stat.stochasticprocess.univariate.integration

This class evaluates the following class of integrals.

IntegralDt(FiltrationFunction) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralDt

Construct an integral for f with respect to dt.

IntegralExpectation - Class in dev.nm.stat.stochasticprocess.univariate.integration

This class computes the expectation of the following class of integrals.

IntegralExpectation(Integral, double, double, int, int, long) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation

Compute the expectation for the integral of a stochastic process.

IntegralExpectation(Integral, double, double, int, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation

Compute the expectation for the integral of a stochastic process.

integrate(ODE1stOrder, double[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.ODEIntegrator

This is the integration method that approximates the solution of a first order ODE.

integrate(ODE1stOrder, double[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.ChangeOfVariable

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.GaussianQuadrature

integrate(UnivariateRealFunction, double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.Integrator

Integrate function f from a to b, \[ \int_a^b\! f(x)\, dx \]

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson

integrate(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann

integrate(UnivariateRealFunction, double, double, SubstitutionRule) - Method in class dev.nm.analysis.integration.univariate.riemann.Riemann

Integrate a function, f, from a to b possibly using change of variable.

Integrator - Interface in dev.nm.analysis.integration.univariate.riemann

This defines the interface for the numerical integration of definite integrals of univariate functions.

intercept() - Method in class dev.nm.stat.regression.linear.LMProblem

Checks if an intercept term is added to the linear regression.

interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicInterpolation

interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BicubicSpline

interpolate(BivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BilinearInterpolation

interpolate(BivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGridInterpolation

Construct a real valued function from a grid of observations.

interpolate(MultivariateGrid) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGridInterpolation

Construct a real valued function from a grid of observations.

interpolate(MultivariateGrid) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation

Interpolation - Interface in dev.nm.analysis.curvefit.interpolation.univariate

Interpolation is a method of constructing new data points within the range of a discrete set of known data points.

Interval<T extends Comparable<? super T>> - Class in dev.nm.interval

For a partially ordered set, there is a binary relation, denoted as ≤, that indicates that, for certain pairs of elements in the set, one of the elements precedes the other.

Interval(T, T) - Constructor for class dev.nm.interval.Interval

Construct an interval.

IntervalRelation - Enum in dev.nm.interval

Allen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.

Intervals<T extends Comparable<? super T>> - Class in dev.nm.interval

This is a disjoint set of intervals.

Intervals() - Constructor for class dev.nm.interval.Intervals

Construct an empty set of intervals.

Intervals(Interval<T>) - Constructor for class dev.nm.interval.Intervals

Construct a set that contains only one interval.

Intervals(T, T) - Constructor for class dev.nm.interval.Intervals

Construct a set that contains only one interval [begin, end].

Intervals(Interval<T>...) - Constructor for class dev.nm.interval.Intervals

Construct a set of intervals.

Intervals(Intervals<T>) - Constructor for class dev.nm.interval.Intervals

Copy constructor.

IntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime

This is a univariate time series indexed by integers.

IntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime

This is the TimeSeries.Entry for an integer number -indexed univariate time series.

intValue() - Method in class dev.nm.number.complex.Complex

Deprecated.
Invalid operation.

intValue() - Method in class dev.nm.number.Real

intValue() - Method in class dev.nm.number.ScientificNotation

InvalidLicense - Error in dev.nm.misc.license

This is the LicenseError thrown when calling a class or method that is not yet licensed.

InvalidLicense(String) - Constructor for error dev.nm.misc.license.InvalidLicense

invdet() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.HilbertMatrix

One over the determinant of H: 1/|H|, which is an integer.

Inverse - Class in dev.nm.algebra.linear.matrix.doubles.operation

For a square matrix A, the inverse, A^-1, if exists, satisfies A.multiply(A.inverse()) == A.ONE() There are multiple ways to compute the inverse of a matrix.

Inverse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Inverse

Constructs the inverse of a matrix.

Inverse(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Inverse

Constructs the inverse of a matrix.

inverse() - Method in interface dev.nm.algebra.structure.Field

For each a in F, there exists an element b in F such that a × b = b × a = 1.

inverse() - Method in class dev.nm.number.complex.Complex

inverse() - Method in class dev.nm.number.Real

INVERSE - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog

inverse(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.link.LinkFunction

Inverse of the link function, i.e., g^-1(x).

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit

Inverse of the link function, i.e., g^-1(x).

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit

inverse(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt

INVERSE_FINE_STRUCTURE_ALPHA1 - Static variable in class dev.nm.misc.PhysicalConstants

The inverse fine-structure constant \(\alpha^{-1}\) (dimensionless).

inverseCovariance() - Method in interface dev.nm.stat.covariance.covarianceselection.CovarianceSelectionSolver

Get the estimated inverse Covariance matrix of the selection problem.

inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST

Gets the inverse of the estimated covariance matrix.

inverseCovariance() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO

Get the inverse of the estimated covariance matrix.

inverseCovariance() - Method in class tech.nmfin.meanreversion.daspremont2008.CovarianceEstimation

InverseIteration - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen

Inverse iteration is an iterative eigenvalue algorithm.

InverseIteration(Matrix, double, InverseIteration.StoppingCriterion) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration

Construct an instance of InverseIteration to find the corresponding eigenvector.

InverseIteration(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration

Construct an instance of InverseIteration to find the corresponding eigenvector.

InverseIteration.StoppingCriterion - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen

This interface defines the convergence criterion.

InverseNonExistent() - Constructor for exception dev.nm.algebra.structure.Field.InverseNonExistent

Construct an instance of InverseNonExistent

InverseTransformSampling - Class in dev.nm.stat.random.rng.univariate

Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc.) is a basic method for pseudo-random number sampling, i.e.

InverseTransformSampling(ProbabilityDistribution, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling

Construct a random number generator to sample from a distribution.

InverseTransformSampling(ProbabilityDistribution) - Constructor for class dev.nm.stat.random.rng.univariate.InverseTransformSampling

Construct a random number generator to sample from a distribution.

InverseTransformSamplingEVDRNG - Class in dev.nm.stat.evt.evd.univariate.rng

Generate random numbers according to a given univariate extreme value distribution, by inverse transform sampling.

InverseTransformSamplingEVDRNG(UnivariateEVD) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG

Create an instance with the given extreme value distribution.

InverseTransformSamplingEVDRNG(UnivariateEVD, long...) - Constructor for class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG

Create an instance with the given extreme value distribution and seeds for this random number generator.

InverseTransformSamplingExpRNG - Class in dev.nm.stat.random.rng.univariate.exp

This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method.

InverseTransformSamplingExpRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG

Construct a random number generator to sample from the exponential distribution using the inverse transform sampling method.

InverseTransformSamplingExpRNG() - Constructor for class dev.nm.stat.random.rng.univariate.exp.InverseTransformSamplingExpRNG

Construct a random number generator to sample from the standard exponential distribution using the inverse transform sampling method.

InverseTransformSamplingGammaRNG - Class in dev.nm.stat.random.rng.univariate.gamma

Deprecated.
There exist much more efficient algorithms.

InverseTransformSamplingGammaRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG

Deprecated.

Construct a random number generator to sample from the gamma distribution using the inverse transform sampling method.

InverseTransformSamplingGammaRNG() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.InverseTransformSamplingGammaRNG

Deprecated.

Construct a random number generator to sample from the standard gamma distribution using the inverse transform sampling method.

InverseTransformSamplingTruncatedNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal.truncated

A random variate x defined as \[ x = \Phi^{-1}( \Phi(\alpha) + U\cdot(\Phi(\beta)-\Phi(\alpha)))\sigma + \mu \] with \(\Phi\) the cumulative distribution function and \(\Phi^{-1}\) its inverse, U a uniform random number on (0, 1), follows the distribution truncated to the range (a, b).

InverseTransformSamplingTruncatedNormalRNG(double, double, double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.

InverseTransformSamplingTruncatedNormalRNG(double, double, double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

Construct a rng that samples from a truncated Normal distribution using inverse sampling technique.

InverseTransformSamplingTruncatedNormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

Construct a rng that samples from a truncated standard Normal distribution using inverse sampling technique.

InvertingVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution

This is the inverting-variable transformation.

InvertingVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable

Construct an InvertingVariable substitution rule.

invOfwAtwA() - Method in class dev.nm.stat.regression.linear.LMProblem

(wA' * wA)^-1

IPMinimizer<T extends IPProblem,S extends MinimizationSolution<Vector>> - Interface in dev.nm.solver.multivariate.constrained.integer

An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints.

IPProblem - Interface in dev.nm.solver.multivariate.constrained.integer

An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers.

IPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.integer

This is an implementation of a general Integer Programming problem in which some variables take only integers.

IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[], double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1

Construct a constrained optimization problem with integral constraints.

IPProblemImpl1(RealScalarFunction, EqualityConstraints, LessThanConstraints, int[]) - Constructor for class dev.nm.solver.multivariate.constrained.integer.IPProblemImpl1

Construct a constrained optimization problem with integral constraints.

ir() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights

is(IntervalRelation, Interval<T>) - Method in class dev.nm.interval.Interval

Check whether this and Y satisfies a certain Allen's interval relation.

isAcyclic(UnDiGraph<V, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils

Check if an undirected graph is acyclic.

isAcyclic() - Method in class dev.nm.graph.type.SparseDAGraph

Runs validity check to ensure that this DA graph is indeed acyclic.

isAllZeros(double[], double) - Static method in class dev.nm.number.DoubleUtils

Check if a double array contains only 0s, entry-by-entry.

isArray(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is a row or a column.

isBalanced() - Method in class dev.nm.stat.regression.linear.panel.PanelData

Checks if the panel is balanced, i.e., all subjects have the same number of observations (times).

isBetween(Interval<T>, Interval<T>) - Method in enum dev.nm.interval.IntervalRelation

Check if X and Y satisfy a certain relation.

isBracketing(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

Check whether [xl, xu] is bracketing x.

isCandidate() - Method in interface dev.nm.misc.algorithm.bb.BBNode

Check if this node is a possible solution to the original problem, e.g., not pruned.

isCandidate() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode

isColumn(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is a column.

isConnected(UnDiGraph<V, ? extends UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils

Check whether an undirected graph is connected.

isConverged() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

This is the convergence criterion.

isConverged() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

This genetic algorithm terminates if the minimum does not improve for a fixed number of iterations, or the maximum number of iterations is exceeded.

isConverged() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionGLASSOFAST

Checks if the algorithm converges.

isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS

Checks if the graph is cyclic.

isCyclic - Variable in class dev.nm.graph.algorithm.traversal.DFS.Node

isCyclic() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node

Check whether this node is on a cyclic path of the graph.

isDiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a square matrix is a diagonal matrix, up to a precision.

isEmpty() - Method in class dev.nm.graph.community.EdgeBetweeness

Checks if there is no edge, e.g., all vertices are isolated.

isEmpty() - Method in interface dev.nm.misc.algorithm.bb.ActiveList

Returns true if this collection contains no elements.

isEmpty() - Method in class dev.nm.misc.datastructure.IdentityHashSet

isFat(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is fat.

isFeasible() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Check if this table is feasible.

isFixedIndex(int, Map<Integer, Double>) - Static method in class dev.nm.analysis.function.SubFunction

Checks whether a particular index corresponds a fixed variable/value.

isFixedIndex(int) - Method in class dev.nm.analysis.function.SubFunction

Checks whether a particular index corresponds a fixed variable/value.

isFree(int) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblem

Check whether x_i is a free variable after handling the box constraints.

isFree(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

isFree(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem

isFree(int) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.problem.ILPProblemImpl1

isHessenberg(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

Check if H is upper Hessenberg.

isIdempotent(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is idempotent.

isIdentity(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is an identity matrix, up to a precision.

isInBox(Vector, Vector, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

Check if a solution is within a box.

isInfinite(Complex) - Static method in class dev.nm.number.complex.Complex

Check if a complex number is an infinity; i.e., either the real or the imaginary part is infinite, c.f., Double.isInfinite(), and the number is not a NaN.

isInKernel(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Deprecated.
Not supported yet.

isLASSO() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Checks if the LASSO variation of LARS is used.

isLowerBidiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is lower bidiagonal, up to a precision.

isLowerTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is lower triangular, up to a precision.

isMagicSquare(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Deprecated.
Not supported yet.

isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the convergence criterion

isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

the convergence criterion

isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution

This algorithm stops only after a pre-specified number of iterations.

isMinFound() - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer.Solution

isNaN(Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a vector contains any NaN entry.

isNaN(Complex) - Static method in class dev.nm.number.complex.Complex

Check if a complex number is an NaN; i.e., either the real or the imaginary part is an NaN.

isNegative(double, double) - Static method in class dev.nm.number.DoubleUtils

Check if d is negative.

isNegligible(Matrix, int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DefaultDeflationCriterion

Checks if H[i,j] is negligible by Steward's deflation criterion.

isNegligible(Matrix, int, int, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.DeflationCriterion

Checks whether a sub-diagonal element is sufficiently small.

isNullOrEmpty(String) - Static method in class dev.nm.misc.StringUtils

Checks if a string is either null or empty.

isNullRejected(double) - Method in class dev.nm.stat.test.HypothesisTest

Use the p-value to check whether the null hypothesis can be rejected for a given significance level.

isNumber(double) - Static method in class dev.nm.number.DoubleUtils

Check if a double is a number, i.e., it is not ∞ or NaN.

isOK() - Method in class tech.nmfin.meanreversion.cointegration.check.CorrelationCheck

isOK() - Method in interface tech.nmfin.meanreversion.cointegration.check.PairingCheck

isOrthogonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is orthogonal, up to a precision.

isPositive(double, double) - Static method in class dev.nm.number.DoubleUtils

Check if d is positive.

isPositiveDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a square matrix is positive definite; the matrix needs not be symmetric.

isPositiveSemiDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a square matrix is positive definite, up to a precision.

isPow2(int) - Static method in class dev.nm.number.DoubleUtils

Check if an integer is a power of 2.

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

Decides whether the given proposed state should be accepted, or whether the system should remain in it's current state.

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings

isProposalAccepted(RealScalarFunction, RandomLongGenerator, Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisUtils

Uses the given LOG density function to determine whether the given state transition should be accepted.

isProposalAccepted(Vector, Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis

isQuasiTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is quasi (upper) triangular, up to a precision.

isReal(Complex) - Static method in class dev.nm.number.complex.Complex

Check if this complex number is a real number; i.e., the imaginary part is 0.

isReal(Number) - Static method in class dev.nm.number.NumberUtils

Check if a number is a real number.

isReducedRowEchelonForm(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is in the reduced row echelon form, up to a precision.

isReducible(Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

Check if H is upper Hessenberg and is reducible.

isReducible() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints

Check if we can reduce the number of linear equalities.

isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.AbsoluteTolerance

isResidualSmall(double) - Method in class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance

isResidualSmall(double) - Method in interface dev.nm.misc.algorithm.iterative.tolerance.Tolerance

Checks if the updated residual satisfies the tolerance criteria.

isRow(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is a row.

isRowEchelonForm(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is in the row echelon form, up to a precision.

isSameDimension(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if two tables have the same dimension.

isSatisfied(Constraints, Vector, double) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils

Checks if the constraints are satisfied.

isSatisfied(Constraints, Vector) - Static method in class dev.nm.solver.multivariate.constrained.constraint.ConstraintsUtils

Checks if the constraints are satisfied.

isScalar(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Deprecated.
Not supported yet.

isSelected(int) - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection

Checks whether a particular indexed factor is selected in the model.

isSingular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a square matrix is singular, i.e, having no inverse, up to a precision.

isSkewSymmetric(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is skew symmetric.

isSpanned(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Check whether a vector is in the span of the basis.

isSquare(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is square.

isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterIterations

isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AfterNoImprovement

isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AndStopConditions

isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.AtThreshold

isStopped(Vector, double...) - Method in class dev.nm.misc.algorithm.stopcondition.OrStopConditions

isStopped(Vector, double...) - Method in interface dev.nm.misc.algorithm.stopcondition.StopCondition

This is called after each iteration to determine whether the termination conditions are met, e.g., convergence.

isStronglyConnected(DiGraph<V, ? extends Arc<V>>) - Static method in class dev.nm.graph.GraphUtils

Check whether a directed graph is strongly connected.

isSymmetric(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is symmetric.

isSymmetricPositiveDefinite(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a square matrix is symmetric and positive definite.

isTall(Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Check if a table is tall.

isTridiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is tridiagonal, up to a precision.

isTrue(double, int) - Method in interface dev.nm.number.DoubleUtils.which

Decide whether x is to be selected.

isUnique() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution

Return true if the quadratic programming problem has only one solution.

isUnreduced(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

A bi-diagonal matrix is unreduced if it has no 0 on both the super and main diagonals.

isUpperBidiagonal(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is upper bidiagonal, up to a precision.

isUpperTriangular(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a matrix is upper triangular, up to a precision.

isValidated(Matrix) - Method in class dev.nm.stat.test.distribution.pearson.AS159

Checks whether a matrix satisfies the row and column sums.

isWeekend(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Check if the given time is a weekend.

isZero() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Check if the kernel has zero dimension, that is, if A has full rank.

isZero(Vector, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if a vector is a zero vector, i.e., all its entries are 0, up to a precision.

isZero(double, double) - Static method in class dev.nm.number.DoubleUtils

Check if d is zero.

iter - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

iter - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the current iteration count

iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.AntoniouLu2007

iterate(PrimalDualSolution, Vector, Vector, double) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.SDPT3v4

IteratesMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor

This IterationMonitor stores all states generated during iterations.

IteratesMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.IteratesMonitor

Construct a monitor to keep track of the states in all iterations.

iteration - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

iteration(Elliott2005DLM, double[]) - Static method in class tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter

IterationBody<T> - Interface in dev.nm.misc.parallel

This interface defines the code snippet to be run in parallel.

IterationMonitor<S> - Interface in dev.nm.misc.algorithm.iterative.monitor

To debug an iterative algorithm, such as in IterativeMethod, it is useful to keep track of the all states generated in the iterations.

IterativeC2Maximizer - Class in dev.nm.solver.multivariate.unconstrained.c2

A maximization problem is simply minimizing the negative of the objective function.

IterativeC2Maximizer(T) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer

Construct a multivariate maximizer to maximize an objective function.

IterativeC2Maximizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer

Construct a multivariate maximizer to maximize an objective function.

IterativeC2Maximizer.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2

IterativeC2Minimizer - Interface in dev.nm.solver.multivariate.unconstrained.c2

This is a minimizer that minimizes a twice continuously differentiable, multivariate function.

IterativeCentralDifference - Class in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2

An iterative central difference algorithm to obtain a numerical approximation to Poisson's equations with Dirichlet boundary conditions.

IterativeCentralDifference(double, int) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference

Create an instance of this method with the given error bound as the convergence criterion, and the maximum number of iterations allowed.

IterativeIntegrator - Interface in dev.nm.analysis.integration.univariate.riemann

An iterative integrator computes an integral by a series of sums, which approximates the value of the integral.

IterativeLinearSystemSolver - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative

An iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications.

IterativeLinearSystemSolver.Solution - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative

This is the solution to a system of linear equations using an iterative solver.

IterativeMethod<S> - Interface in dev.nm.misc.algorithm.iterative

An iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems.

IterativeMinimizer - Interface in dev.nm.solver.multivariate.unconstrained

This is an iterative multivariate minimizer.

IterativeSolution<S> - Interface in dev.nm.solver

Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution.

iterator() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

iterator() - Method in class dev.nm.combinatorics.Twiddle

iterator() - Method in class dev.nm.misc.algorithm.CartesianProduct

iterator() - Method in class dev.nm.misc.datastructure.IdentityHashSet

iterator() - Method in class dev.nm.misc.datastructure.MultiDimensionalGrid

iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint

iterator() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints

iterator() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries

iterator() - Method in class dev.nm.stat.dlm.univariate.DLMSeries

iterator() - Method in class dev.nm.stat.random.rng.ConstantSeeder

iterator() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.DynamicCreator

iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid

iterator() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid

iterator() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

iterator() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries

IWLS - Class in dev.nm.stat.regression.linear.glm

This implementation estimates parameters β in a GLM model using the Iteratively Re-weighted Least Squares algorithm.

IWLS(double, int) - Constructor for class dev.nm.stat.regression.linear.glm.IWLS

Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.

J

j() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Gets the value of j.

j - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate

the column index

Jacobian - Class in dev.nm.analysis.differentiation.multivariate

The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.

Jacobian(RealVectorFunction, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian

Construct the Jacobian matrix for a multivariate function f at point x.

Jacobian(RealScalarFunction[], Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian

Construct the Jacobian matrix for a multivariate function f at point x.

Jacobian(List<RealScalarFunction>, Vector) - Constructor for class dev.nm.analysis.differentiation.multivariate.Jacobian

Construct the Jacobian matrix for a multivariate function f at point x.

JacobianFunction - Class in dev.nm.analysis.differentiation.multivariate

The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x.

JacobianFunction(RealVectorFunction) - Constructor for class dev.nm.analysis.differentiation.multivariate.JacobianFunction

Construct the Jacobian function of a real scalar function f.

JacobiPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

The Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal of the coefficient matrix, i.e., P = diag(A).

JacobiPreconditioner(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner

Construct a Jacobi preconditioner.

JacobiSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary

The Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration.

JacobiSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver

Construct a Jacobi solver.

JarqueBera - Class in dev.nm.stat.test.distribution.normality

The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness.

JarqueBera(double[], boolean) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera

Perform the Jarque-Bera test to test for the departure from normality.

JarqueBera(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBera

Perform the Jarque-Bera test to test for the departure from normality, using the asymptotic chi-square distribution.

JarqueBeraDistribution - Class in dev.nm.stat.test.distribution.normality

Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality.

JarqueBeraDistribution(int, int, StandardNormalRNG) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution

Construct a Jarque-Bera distribution using Monte Carlo simulation.

JarqueBeraDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.normality.JarqueBeraDistribution

Construct a Jarque-Bera distribution using Monte Carlo simulation.

JenkinsTraubReal - Class in dev.nm.analysis.function.polynomial.root.jenkinstraub

The Jenkins-Traub algorithm is a fast globally convergent iterative method for solving for polynomial roots.

JenkinsTraubReal() - Constructor for class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal

JodaTimeUtils - Class in dev.nm.misc.datastructure.time

These are the utility functions to manipulate JodaTime.

JohansenAsymptoticDistribution - Class in dev.nm.stat.cointegration

Johansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests), each for 5 different trend types.

JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int, long) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution

Construct the asymptotic distribution of a Johansen test.

JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution

Construct the asymptotic distribution of a Johansen test.

JohansenAsymptoticDistribution(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenAsymptoticDistribution

Construct the asymptotic distribution of a Johansen test.

JohansenAsymptoticDistribution.F - Interface in dev.nm.stat.cointegration

This is a filtration function.

JohansenAsymptoticDistribution.Test - Enum in dev.nm.stat.cointegration

the available types of Johansen cointegration tests

JohansenAsymptoticDistribution.TrendType - Enum in dev.nm.stat.cointegration

the available types of trends

JohansenTest - Class in dev.nm.stat.cointegration

The maximum number of cointegrating relations among a multivariate time series is the rank of the Π matrix.

JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int, int, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest

Construct an instance of JohansenTest.

JohansenTest(JohansenAsymptoticDistribution.Test, JohansenAsymptoticDistribution.TrendType, int) - Constructor for class dev.nm.stat.cointegration.JohansenTest

Construct an instance of JohansenTest.

JordanExchange - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex

Jordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A.

JordanExchange() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange

K

K - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of distinct population eigenvalues bigger than 0; the length of t

k - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda

the shape parameter

k(Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC

Evaluates the standard kinetic energy, k = p^2 / 2m.

K(ARMAModel) - Static method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep

k - Variable in class tech.nmfin.infantino2010.Infantino2010PCA

K() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

Gets the H property.

Kagi<T extends Comparable<? super T>> - Class in tech.nmfin.meanreversion.hvolatility

KAGI construction of a random process.

Kagi(UnivariateTimeSeries<T, ? extends UnivariateTimeSeries.Entry<T>>) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi

Kagi(double[]) - Constructor for class tech.nmfin.meanreversion.hvolatility.Kagi

Kagi.Trend - Enum in tech.nmfin.meanreversion.hvolatility

KagiModel - Class in tech.nmfin.meanreversion.hvolatility

Maintains the states of a KAGI model.

KagiModel(double) - Constructor for class tech.nmfin.meanreversion.hvolatility.KagiModel

KendallRankCorrelation - Class in dev.nm.stat.descriptive.correlation

The Kendall rank correlation coefficient, commonly referred to as Kendall's tau (τ) coefficient, is a statistic used to measure the association between two measured quantities.

KendallRankCorrelation(double[], double[]) - Constructor for class dev.nm.stat.descriptive.correlation.KendallRankCorrelation

Construct a Kendall rank calculator initialized with two samples.

Kernel - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

The kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0.

Kernel(Matrix, Kernel.Method, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Construct the kernel of a matrix.

Kernel(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Construct the kernel of a matrix.

Kernel.Method - Enum in dev.nm.algebra.linear.matrix.doubles.linearsystem

These are the available methods to compute kernel basis.

keySet() - Method in class dev.nm.combinatorics.Counter

Get the set of numbers the counter has seen.

KnightSatchellTran1995 - Class in tech.nmfin.trend.kst1995

Implements the Knight-Satchell-Tran model of financial asset returns.

KnightSatchellTran1995(double, double, double, double, double, double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Constructs an instance of the Knight-Satchell-Tran model of returns.

KnightSatchellTran1995(double, double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Constructs an instance of the Knight-Satchell-Tran model of returns.

KnightSatchellTran1995(KnightSatchellTran1995) - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Copy constructor.

KnightSatchellTran1995MLE - Class in tech.nmfin.trend.kst1995

Fits a KST model from returns.

KnightSatchellTran1995MLE() - Constructor for class tech.nmfin.trend.kst1995.KnightSatchellTran1995MLE

Knuth1969 - Class in dev.nm.stat.random.rng.univariate.poisson

This is a random number generator that generates random deviates according to the Poisson distribution.

Knuth1969(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969

Construct a random number generator to sample from the Poisson distribution.

Knuth1969(double) - Constructor for class dev.nm.stat.random.rng.univariate.poisson.Knuth1969

Construct a random number generator to sample from the Poisson distribution.

KolmogorovDistribution - Class in dev.nm.stat.test.distribution.kolmogorov

The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic.

KolmogorovDistribution(int, int, boolean) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Construct a Kolmogorov distribution for a sample size n.

KolmogorovDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Construct a Kolmogorov distribution for a sample size n.

KolmogorovOneSidedDistribution - Class in dev.nm.stat.test.distribution.kolmogorov

Compute the probability that F(x) is dominated by the upper confidence contour, for all x: Pn(ε) = Pr{F(x) < min{Fn(x) + ε, 1}}

KolmogorovOneSidedDistribution(int, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Construct a one-sided Kolmogorov distribution.

KolmogorovOneSidedDistribution(int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Construct a one-sided Kolmogorov distribution.

KolmogorovSmirnov - Class in dev.nm.stat.test.distribution.kolmogorov

The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test).

KolmogorovSmirnov.Side - Enum in dev.nm.stat.test.distribution.kolmogorov

the available types of the Kolmogorov-Smirnov statistic

KolmogorovSmirnov.Type - Enum in dev.nm.stat.test.distribution.kolmogorov

the available types of the Kolmogorov-Smirnov tests

KolmogorovSmirnov1Sample - Class in dev.nm.stat.test.distribution.kolmogorov

The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution.

KolmogorovSmirnov1Sample(double[], ProbabilityDistribution, KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample

Construct a one-sample Kolmogorov-Smirnov test.

KolmogorovSmirnov2Samples - Class in dev.nm.stat.test.distribution.kolmogorov

The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples.

KolmogorovSmirnov2Samples(double[], double[], KolmogorovSmirnov.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples

Construct a two-sample Kolmogorov-Smirnov test.

KolmogorovTwoSamplesDistribution - Class in dev.nm.stat.test.distribution.kolmogorov

Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test.

KolmogorovTwoSamplesDistribution(int, int, double[], KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Construct a two-sample Kolmogorov distribution.

KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, int) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Construct a two-sample Kolmogorov distribution, assuming that there is no tie in the samples.

KolmogorovTwoSamplesDistribution(int, int, KolmogorovTwoSamplesDistribution.Side, double[]) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Construct a two-sample Kolmogorov distribution.

KolmogorovTwoSamplesDistribution(double[], double[], KolmogorovTwoSamplesDistribution.Side) - Constructor for class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Construct a two-sample Kolmogorov distribution.

KolmogorovTwoSamplesDistribution.Side - Enum in dev.nm.stat.test.distribution.kolmogorov

the available types of Kolmogorov-Smirnov two-sample test

KroneckerProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation

Given an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, is an (mp)-by-(nq) matrix with entries defined by c_st = a_ij b_kl where

KroneckerProduct(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.KroneckerProduct

Construct the Kronecker product of two matrices.

KruskalWallis - Class in dev.nm.stat.test.rank

The Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups.

KruskalWallis(double[]...) - Constructor for class dev.nm.stat.test.rank.KruskalWallis

Construct a Kruskal-Wallis test for the equality of medians of groups.

KunduGupta2007 - Class in dev.nm.stat.random.rng.univariate.gamma

Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, when the shape parameter lies between 0 and 1.

KunduGupta2007(double, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007

Construct a random number generator to sample from the gamma distribution.

KunduGupta2007(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007

Construct a random number generator to sample from the gamma distribution.

Kurtosis - Class in dev.nm.stat.descriptive.moment

Kurtosis measures the "peakedness" of the probability distribution of a real-valued random variable.

Kurtosis() - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis

Construct an empty Kurtosis calculator.

Kurtosis(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis

Construct a Kurtosis calculator, initialized with a sample.

Kurtosis(Kurtosis) - Constructor for class dev.nm.stat.descriptive.moment.Kurtosis

Copy constructor.

kurtosis() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.FDistribution

Gets the excess kurtosis of this distribution.

kurtosis() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

kurtosis() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the excess kurtosis of this distribution.

kurtosis() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.TDistribution

Gets the excess kurtosis of this distribution.

kurtosis() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

kurtosis() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

kurtosis() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

kurtosis() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

kurtosis() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Deprecated.

kurtosis() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Deprecated.

L

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

The sub-diagonal entries of the unit lower triangular matrix L.

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination

Get the lower triangular matrix L, such that P * A = L * U.

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol

L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Cholesky

Get the lower triangular matrix L.

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewicz

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskySparse

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyWang2006

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt

Get L as in the LDL decomposition.

L() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU

L() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition

Get the lower triangular matrix L as in the LU decomposition.

L() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies

Gets the lower triangular matrix L in the LDL decomposition.

Label(SimplexTable.LabelType, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label

Construct a label for a row or column in the simplex table.

lag(int, int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.

lag(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries by lagging the time series.

lag(int, int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

Constructs an instance of SimpleTimeSeries by lagging the time series.

lag(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

Constructs an instance of SimpleTimeSeries by lagging the time series.

LaguerrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

Laguerre polynomials are defined by the recurrence relation below.

LaguerrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerrePolynomials

LaguerreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

LaguerreRule(int, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule

Create a Laguerre rule of the given order.

Lai2010NPEBModel - Class in tech.nmfin.portfoliooptimization.lai2010

The Non-Parametric Empirical Bayes (NPEB) model described in the reference computes the optimal weights for asset allocation.

Lai2010NPEBModel(MVOptimizer, int) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel

Constructs an instance of the model using MomentsEstimatorLedoitWolf as the default estimator of covariance matrix, and assuming IID for each individual asset for re-sampling.

Lai2010NPEBModel(MVOptimizer, int, ReturnsMoments.Estimator, ReturnsResamplerFactory, CetaMaximizer) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel

Constructs an instance of the model.

Lai2010NPEBModel.OptimalWeights - Class in tech.nmfin.portfoliooptimization.lai2010

Lai2010OptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization

Lai2010OptimizationAlgorithm(MVOptimizer, ReturnsMoments.Estimator, ReturnsResamplerFactory, int, double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm

Lai2010OptimizationAlgorithm(double) - Constructor for class tech.nmfin.portfoliooptimization.Lai2010OptimizationAlgorithm

lambda - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext

The norm of the generator with the sign chosen to be the opposite of the first coordinate of the generator.

lambda() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result

Gets sample eigenvalues.

Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution.Lambda

Stores the Beta distribution parameters.

Lambda(int, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda

Stores the Binomial distribution parameters.

Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda

Stores the Gamma distribution parameters.

Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda

Construct a Log-Normal distribution.

Lambda(double, double) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda

Construct a Normal distribution.

lambda() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem

Gets the penalization parameter for the unconstrained form of LASSO.

lambda() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint

lambda1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the transition intensity from bull market to bear market.

lambda1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

lambda2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the transition intensity from bear market to bull market.

lambda2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

lambda_Jacobian - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

lambda Jacobian

lambdaCol - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

Lanczos - Class in dev.nm.analysis.function.special.gamma

The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964.

Lanczos(double, int, int) - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos

Construct a Lanczos approximation instance.

Lanczos() - Constructor for class dev.nm.analysis.function.special.gamma.Lanczos

Construct a Lanczos approximation instance using default parameters.

LARSFitting - Class in dev.nm.stat.regression.linear.lasso.lars

This class computes the entire LARS sequence of coefficients and fits, starting from zero to the OLS fit.

LARSFitting(LARSProblem, double, int) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting

Estimates the entire LARS sequence of coefficients and fits, starting from zero to the OLS fit.

LARSFitting(LARSProblem, int) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting

Estimates the entire LARS sequence of coefficients with the default epsilon.

LARSFitting(LARSProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSFitting

Estimates the entire LARS sequence of coefficients with the default epsilon and maximum number of steps.

LARSFitting.Estimators - Class in dev.nm.stat.regression.linear.lasso.lars

LARSProblem - Class in dev.nm.stat.regression.linear.lasso.lars

Least Angle Regression (LARS) is a regression algorithm for high-dimensional data.

LARSProblem(Vector, Matrix, boolean, boolean, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Constructs a Least Angel Regression (LARS) problem.

LARSProblem(Vector, Matrix, boolean, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model.

LARSProblem(Vector, Matrix, boolean) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.

LARSProblem(Vector, Matrix) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Constructs a LASSO variation of the Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.

LARSProblem(LARSProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Copy constructor.

lastEdge(V) - Method in class dev.nm.graph.algorithm.shortestpath.Dijkstra

lastEdge(V) - Method in interface dev.nm.graph.algorithm.shortestpath.ShortestPath

Gets the last edge of a vertex on its shortest distance from the source.

lcm(int, int) - Static method in class dev.nm.analysis.function.FunctionOps

Calculates the least common multiple of integer a and integer b.

LD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

LDDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Represents a L D L^T decomposition of a shifted symmetric tridiagonal matrix T.

LDDecomposition(Vector, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

LDFactorizationFromRoot - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Decomposes (T - σ I) into L D L^T where T is a symmetric tridiagonal matrix, σ is a shift for this factorization, L is a unit lower triangular matrix, and D is a diagonal matrix.

LDFactorizationFromRoot(Vector, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDFactorizationFromRoot

Creates a decomposition for a symmetric tridiagonal matrix T.

LDLt - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle

The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * L^t.

LDLt(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt

Run the LDL decomposition on a real and symmetric (hence square) matrix.

LDLt(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt

Run the LDL decomposition on a real and symmetric (hence square) matrix.

LeapFrogging - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid

The leap-frogging algorithm is a method for simulating Molecular Dynamics, which is time-reversible.

LeapFrogging(RealVectorFunction, Vector, Vector, Vector, double) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Constructs a new instance with the given parameters.

LeapFrogging.DynamicsState - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid

Contains the entire state (both the position and the momentum) at a given point in time.

LeastPth<T> - Class in dev.nm.solver.multivariate.minmax

The least p-th minmax algorithm minimizes the maximal error/loss (function): \[ \min_x \max_{\omega \in S} e(x, \omega) \] \(e(x, \omega)\) is the error or loss function.

LeastPth(double, int) - Constructor for class dev.nm.solver.multivariate.minmax.LeastPth

Construct a minmax minimizer using the Least p-th method.

LeastSquares - Class in dev.nm.analysis.curvefit

This method obtains a least squares estimate of a polynomial to fit the input data, by a weighted sum of orthogonal polynomials up to a specified order.

LeastSquares(int, LeastSquares.Weighting) - Constructor for class dev.nm.analysis.curvefit.LeastSquares

Construct a new instance of this algorithm, which will use a weighted sum of orthogonal polynomials up to order n (the number of points).

LeastSquares(int) - Constructor for class dev.nm.analysis.curvefit.LeastSquares

Construct a new instance of this algorithm, which uses uniform weighting for the observations.

LeastSquares.Weighting - Interface in dev.nm.analysis.curvefit

This interface defines a weighting for observations.

Lebesgue - Class in dev.nm.analysis.integration.univariate

Lebesgue integration is the general theory of integration of a function with respect to a general measure.

Lebesgue(double[], double[]) - Constructor for class dev.nm.analysis.integration.univariate.Lebesgue

Construct a Lebesgue integral.

LEcuyer - Class in dev.nm.stat.random.rng.univariate.uniform.linear

This is the uniform random number generator recommended by L'Ecuyer in 1996.

LEcuyer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

Construct a LEcuyer pseudo uniform random generator.

LEcuyer(long, long, long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

Construct a LEcuyer pseudo uniform random generator and then seed.

LedoitWolf2004 - Class in dev.nm.stat.covariance

To estimate the covariance matrix, Ledoit and Wolf (2004) suggests using the matrix obtained from the sample covariance matrix through a transformation called shrinkage.

LedoitWolf2004() - Constructor for class dev.nm.stat.covariance.LedoitWolf2004

Creates the algorithm instance, using an unbiased sample covariance matrix by default.

LedoitWolf2004(boolean) - Constructor for class dev.nm.stat.covariance.LedoitWolf2004

Creates the algorithm instance, with the option to use an unbiased or biased sample covariance matrix.

LedoitWolf2004.Result - Class in dev.nm.stat.covariance

The estimator and some intermediate values computed by the algorithm.

LedoitWolf2016 - Class in dev.nm.stat.covariance.nlshrink

This is Ledoit's non-linear shrinkage method for computing covariance matrixes when the dimension is large compared to the number of observations.

LedoitWolf2016() - Constructor for class dev.nm.stat.covariance.nlshrink.LedoitWolf2016

LedoitWolf2016(boolean) - Constructor for class dev.nm.stat.covariance.nlshrink.LedoitWolf2016

LedoitWolf2016.Result - Class in dev.nm.stat.covariance.nlshrink

the estimator and some intermediate values computed by the algorithm

leftConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T

Get the one sided left confidence interval, [0, a]

leftConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F

Compute the one sided left confidence interval, [0, a]

leftMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Left multiplies a matrix.

leftOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T

Get the left, one-sided p-value.

leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey

Get the left, one-sided p-value.

leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Get the left, one-sided p-value.

leftOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Get the left, one-sided p-value.

leftOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F

Get the left, one-sided p-value.

leftShift(T[]) - Static method in class dev.nm.misc.ArrayUtils

Get a left shifted array.

leftShift(double...) - Static method in class dev.nm.number.DoubleUtils

Perform a in-memory left-shift (by 1 cell} to an array.

leftShift(double[], int) - Static method in class dev.nm.number.DoubleUtils

Perform a in-memory right-shift (by k cells} to an array.

leftShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils

Get a left shifted (by 1 cell) copy of an array.

leftShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils

Get a left shifted (by k cells) copy of an array.

LegendrePolynomials - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

A Legendre polynomial is defined by the recurrence relation below.

LegendrePolynomials() - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendrePolynomials

LegendreRule - Class in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

LegendreRule(int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule

Create a Legendre rule of the given order.

Lehmer - Class in dev.nm.stat.random.rng.univariate.uniform.linear

Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in [0, 1].

Lehmer(long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

Construct a Lehmer (pure) linear congruential generator.

Lehmer(long, long, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

Construct a skipping ahead Lehmer (pure) linear congruential generator.

Lehmer() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

Construct a Lehmer (pure) linear congruential generator.

length() - Method in class dev.nm.analysis.sequence.Fibonacci

length() - Method in interface dev.nm.analysis.sequence.Sequence

Get the number of computed terms in the sequence.

length() - Method in class dev.nm.geometry.LineSegment

Get the length of the line segment.

LessThanConstraints - Interface in dev.nm.solver.multivariate.constrained.constraint

The domain of an optimization problem may be restricted by less-than or equal-to constraints.

Levene - Class in dev.nm.stat.test.variance

The Levene test tests for the equality of variance of groups.

Levene(double...) - Constructor for class dev.nm.stat.test.variance.Levene

Perform the Levene test to test for equal variances across the groups.

Levene(Levene.Type, double[]...) - Constructor for class dev.nm.stat.test.variance.Levene

Perform the Levene test to test for equal variances across the groups.

Levene.Type - Enum in dev.nm.stat.test.variance

the available implementations when computing the absolute deviations

leverage() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the leverage.

License - Class in dev.nm.misc.license

This is the license management system for the library.

LICENSE_FILE_PROPERTY - Static variable in class dev.nm.misc.license.License

The system property term for setting license file.

LicenseError - Error in dev.nm.misc.license

General error regarding the license, e.g., errors when loading license.

LicenseError(String) - Constructor for error dev.nm.misc.license.LicenseError

LIGHT_SPEED_C - Static variable in class dev.nm.misc.PhysicalConstants

The speed of light in a vacuum \(c\), \(c_0\) in meters per second (m s^-1).

Lilliefors - Class in dev.nm.stat.test.distribution.normality

Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.

Lilliefors(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.Lilliefors

Perform the Lilliefors test to test for the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance.

LILSparseMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

The list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value.

LILSparseMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

Construct a sparse matrix in LIL format.

LILSparseMatrix(int, int, int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

Construct a sparse matrix in LIL format.

LILSparseMatrix(int, int, List<SparseMatrix.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

Construct a sparse matrix in LIL format by a list of non-zero entries.

LILSparseMatrix(LILSparseMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

Copy constructor.

LinearCongruentialGenerator - Interface in dev.nm.stat.random.rng.univariate.uniform.linear

A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation.

LinearConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is a collection of linear constraints for a real-valued optimization problem.

LinearConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints

Construct a collection of linear constraints.

LinearEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is a collection of linear equality constraints.

LinearEqualityConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearEqualityConstraints

Construct a collection of linear equality constraints.

linearEqualityConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint

linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint

Deprecated.

linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling

linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity

linearEqualityConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit

LinearFit - Class in dev.nm.stat.evt.evd.univariate.fitting.acer

Find the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values.

LinearFit() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit

Create an instance with the assumption of c = 2.

LinearFit(double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.LinearFit

This fitting assumes c is a constant.

LinearGreaterThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is a collection of linear greater-than-or-equal-to constraints.

LinearGreaterThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints

Construct a collection of linear greater-than or equal-to constraints.

linearGreaterThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint

linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint

Deprecated.

linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling

linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity

linearGreaterThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit

linearInterpolate(double, double, double, double, double) - Static method in class dev.nm.analysis.function.FunctionOps

Linear interpolation between two points.

LinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.univariate

(Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent data-points.

LinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.LinearInterpolation

LinearInterpolator - Class in dev.nm.analysis.curvefit.interpolation

Define a univariate function by linearly interpolating between adjacent points.

LinearInterpolator(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.LinearInterpolator

Construct a univariate function by linearly interpolating between adjacent points.

LinearKalmanFilter - Class in dev.nm.stat.dlm.univariate

The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.

LinearKalmanFilter(DLM) - Constructor for class dev.nm.stat.dlm.univariate.LinearKalmanFilter

Construct a Kalman filter from a univariate controlled dynamic linear model.

LinearLessThanConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is a collection of linear less-than-or-equal-to constraints.

LinearLessThanConstraints(Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints

Construct a collection of linear less-than or equal-to constraints.

linearLessThanConstraints() - Method in interface tech.nmfin.portfoliooptimization.markowitz.constraints.QPConstraint

linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint

Deprecated.

linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling

linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity

linearLessThanConstraints() - Method in class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit

LinearModel - Interface in dev.nm.stat.regression.linear

A linear model provides fitting and the residual analysis (goodness of fit).

LinearRepresentation - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.

LinearRepresentation(ARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation

Construct the linear representation of an ARMA model.

LinearRepresentation(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.LinearRepresentation

Construct the linear representation of an ARMA model up to the default number of lags LinearRepresentation.DEFAULT_NUMBER_OF_LAGS.

LinearRoot - Class in dev.nm.analysis.function.polynomial.root

This is a solver for finding the roots of a linear equation.

LinearRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.LinearRoot

LinearSystemSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

Solve a system of linear equations in the form: Ax = b, We assume that, after row reduction, A has no more rows than columns.

LinearSystemSolver(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver

Construct a solver for a linear system of equations.

LinearSystemSolver.NoSolution - Exception in dev.nm.algebra.linear.matrix.doubles.linearsystem

This is the runtime exception thrown when it fails to solve a system of linear equations.

LinearSystemSolver.Solution - Interface in dev.nm.algebra.linear.matrix.doubles.linearsystem

This is the solution to a linear system of equations.

LineSearch - Interface in dev.nm.solver.multivariate.unconstrained.c2.linesearch

A line search is often used in another minimization algorithm to improve the current solution in one iteration step.

linesearch(Vector, Vector) - Method in interface dev.nm.solver.multivariate.unconstrained.c2.linesearch.LineSearch.Solution

Get the increment α so that f(x + α * d) is (approximately) minimized.

linesearch - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

LineSearch.Solution - Interface in dev.nm.solver.multivariate.unconstrained.c2.linesearch

This is the solution to a line search minimization.

LineSegment - Class in dev.nm.geometry

Represent a line segment.

LineSegment(Point, Point) - Constructor for class dev.nm.geometry.LineSegment

Create a line segment with two given endpoints.

link() - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMFamily

Get the link function of this distribution.

LinkCloglog - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the complementary log-log link function: g(x) = log(-log(1 - x))

LinkCloglog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkCloglog

LinkFunction - Interface in dev.nm.stat.regression.linear.glm.distribution.link

This interface represents a link function g(x) in Generalized Linear Model (GLM).

LinkIdentity - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the identity link function: g(x) = x

LinkIdentity() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkIdentity

LinkInverse - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the inverse link function: g(x) = 1/x

LinkInverse() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverse

LinkInverseSquared - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the inverse-squared link function: g(x) = 1/x²

LinkInverseSquared() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkInverseSquared

LinkLog - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the log link function: g(x) = log(x)

LinkLog() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLog

LinkLogit - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the logit link function: \[ g(x) = \log(\frac{\mu}{1-\mu}) \]

LinkLogit() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkLogit

LinkProbit - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1).

LinkProbit() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkProbit

LinkSqrt - Class in dev.nm.stat.regression.linear.glm.distribution.link

This class represents the square-root link function: g(x) = sqrt(x)

LinkSqrt() - Constructor for class dev.nm.stat.regression.linear.glm.distribution.link.LinkSqrt

linshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result

Gets linear shrinkage tau in ascending order.

LjungBox - Class in dev.nm.stat.test.timeseries.portmanteau

The Ljung-Box test (named for Greta M.

LjungBox(double[], int, int) - Constructor for class dev.nm.stat.test.timeseries.portmanteau.LjungBox

Perform the Ljung-Box test to check auto-correlation in a time series.

LLD() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

LMBeta - Class in dev.nm.stat.regression.linear

Beta coefficients are the outcomes of fitting a linear regression model.

LMBeta(Vector) - Constructor for class dev.nm.stat.regression.linear.LMBeta

Constructs an instance of Beta.

LMDiagnostics - Class in dev.nm.stat.regression.linear.residualanalysis

This class collects some diagnostics measures for the goodness of fit based on the residulas for a linear regression model.

LMDiagnostics(LMResiduals) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMDiagnostics

Constructs an instance of the Diagnostics from the results of residual analysis.

LMInformationCriteria - Class in dev.nm.stat.regression.linear.residualanalysis

The information criteria measure the goodness of fit of an estimated statistical model.

LMInformationCriteria(LMResiduals) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMInformationCriteria

Computes the information criteria from residual analysis.

LMProblem - Class in dev.nm.stat.regression.linear

This is a linear regression or a linear model (LM) problem.

LMProblem(Vector, Matrix, boolean, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem

Constructs a linear regression problem.

LMProblem(Vector, Matrix, Vector) - Constructor for class dev.nm.stat.regression.linear.LMProblem

Constructs a linear regression problem, assuming a constant term (the intercept).

LMProblem(Vector, Matrix, boolean) - Constructor for class dev.nm.stat.regression.linear.LMProblem

Constructs a linear regression problem, assuming equal weights to all observations.

LMProblem(Vector, Matrix) - Constructor for class dev.nm.stat.regression.linear.LMProblem

Constructs a linear regression problem, assuming a constant term (the intercept) equal weights assigned to all observations

LMProblem(LMProblem) - Constructor for class dev.nm.stat.regression.linear.LMProblem

Copy constructor.

LMResiduals - Class in dev.nm.stat.regression.linear.residualanalysis

This is the residual analysis of the results of a linear regression model.

LMResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Performs residual analysis for a linear regression problem.

loading(int) - Method in interface dev.nm.stat.factor.pca.PCA

Gets the loading vector of the i-th principal component.

loading(int) - Method in class dev.nm.stat.factor.pca.PCAbyEigen

loadings() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Gets the rotated loading matrix.

loadings() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the matrix of variable loadings.

loadings() - Method in class dev.nm.stat.factor.pca.PCAbyEigen

loadings() - Method in class dev.nm.stat.factor.pca.PCAbySVD

LocalSearchCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell

LocalSearchCellFactory> - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local

A LocalSearchCellFactory produces LocalSearchCellFactory.LocalSearchCells.

LocalSearchCellFactory(LocalSearchCellFactory.MinimizerFactory<T>, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory

Construct an instance of a LocalSearchCellFactory.

LocalSearchCellFactory.LocalSearchCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local

A LocalSearchCell implements the two genetic operations.

LocalSearchCellFactory.MinimizerFactory> - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local

This factory constructs a new Minimizer for each mutation operation.

log(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the log of a vector, element-by-element.

log(BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute log(x).

log(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute log(x) up to a scale.

log(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Natural logarithm of a complex number.

log(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the logs of values.

logAcceptanceRatio(RealScalarFunction, Vector, Vector) - Static method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisUtils

Computes the log of the acceptance ratio.

LogBeta - Class in dev.nm.analysis.function.special.beta

This class represents the log of Beta function log(B(x, y)).

LogBeta() - Constructor for class dev.nm.analysis.function.special.beta.LogBeta

logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

logDensity(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

logDensity(double) - Method in interface dev.nm.stat.evt.evd.univariate.UnivariateEVD

Get the logarithm of the probability density function at \(x\), that is, \(\log(f(x))\).

logGamma(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos

Compute log-gamma for a positive value x.

logGamma(BigDecimal) - Method in class dev.nm.analysis.function.special.gamma.Lanczos

Compute log-gamma for a positive value x to arbitrary precision.

LogGamma - Class in dev.nm.analysis.function.special.gamma

The log-Gamma function, \(\log (\Gamma(z))\), for positive real numbers, is the log of the Gamma function.

LogGamma(LogGamma.Method, Lanczos) - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma

Construct an instance of log-Gamma.

LogGamma() - Constructor for class dev.nm.analysis.function.special.gamma.LogGamma

Construct an instance of log-Gamma.

LogGamma.Method - Enum in dev.nm.analysis.function.special.gamma

the available methods to compute \(\log (\Gamma(z))\)

logGammaQuick(double) - Method in class dev.nm.analysis.function.special.gamma.Lanczos

Compute log-gamma for a positive value x.

LogisticBeta - Class in dev.nm.stat.regression.linear.logistic

Beta coefficient estimator, β^, of a logistic regression model.

LogisticBeta(Vector, LogisticResiduals) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticBeta

Construct an instance of Beta.

LogisticProblem - Class in dev.nm.stat.regression.linear.logistic

A logistic regression problem is a variation of the OLS regression problem.

LogisticProblem(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticProblem

Constructs a logistic regression problem from a linear regression problem.

LogisticRegression - Class in dev.nm.stat.regression.linear.logistic

A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve.

LogisticRegression(LogisticProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression

Constructs a Logistic instance.

LogisticRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.logistic.LogisticRegression

Constructs a Logistic instance.

LogisticResiduals - Class in dev.nm.stat.regression.linear.logistic

Residual analysis of the results of a logistic regression.

logLikelihood() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood

Compute the log-likelihood at the fitted value.

logLikelihood() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Gets the log-likelihood value.

logLikelihood() - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure

Gets the likelihood of the given observations.

logLikelihood - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel

the log-likelihood

logLikelihood() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting

logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.IWLS

logLikelihood() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson

logLikelihood(LogisticProblem) - Static method in class dev.nm.stat.regression.linear.logistic.LogisticRegression

Constructs the log-likelihood function for a logistic regression problem.

logMu - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda

the log-mean μ ∈ R

LogNormalDistribution - Class in dev.nm.stat.distribution.univariate

A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed.

LogNormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.LogNormalDistribution

Construct a log-normal distribution.

LogNormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution

The HMM states use the Log-Normal distribution to model the observations.

LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution

Constructs a log-normal distribution for each state in the HMM model.

LogNormalMixtureDistribution(LogNormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution

Constructs a log-normal distribution for each state in the HMM model.

LogNormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution

the log-normal distribution parameters

LogNormalRNG - Class in dev.nm.stat.random.rng.univariate

This random number generator samples from the log-normal distribution.

LogNormalRNG(NormalRNG) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG

Construct a random number generator to sample from the log-normal distribution.

LogNormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.LogNormalRNG

Construct a random number generator to sample from the log-normal distribution.

logProbability(int[], double[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel

Gets the probability of observing the observations and having gone thru the state sequence.

logProbability(int[], int[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel

Gets the probability of observing the observations and having gone thru the state sequence.

logProbability(HmmInnovation[]) - Method in class dev.nm.stat.hmm.HiddenMarkovModel

Gets the probability of observing the observations and having gone thru the state sequence.

logSigma - Variable in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution.Lambda

the log-standard deviation; shape

longValue() - Method in class dev.nm.number.complex.Complex

Deprecated.
Invalid operation.

longValue() - Method in class dev.nm.number.Real

longValue() - Method in class dev.nm.number.ScientificNotation

LoopBody - Interface in dev.nm.misc.parallel

The implementation of this interface contains the code inside a for-loop construct.

lower() - Method in class dev.nm.interval.RealInterval

Get the lower bound of this interval.

lower() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound

lower - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

lowerBound() - Method in class dev.nm.analysis.function.polynomial.CauchyPolynomial

Cauchy's lower bound on polynomial zeros is the unique positive root of the Cauchy polynomial.

lowerBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem

Gets the lower bounds.

lowerBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

Split the box constraints and get the greater-than-the-lower-bounds part.

LowerBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is a lower bound constraints such that for all x_i's, x_i ≥ b

LowerBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.LowerBoundConstraints

Construct a lower bound constraints for all variables in a function.

lowerBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

Gets the lower bounds.

LowerTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle

A lower triangular matrix has 0 entries where column index > row index.

LowerTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

Constructs a lower triangular matrix of dimension dim * dim.

LowerTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

Constructs a lower triangular matrix from a 2D double[][] array.

LowerTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

Constructs a lower triangular matrix from a matrix.

LowerTriangularMatrix(LowerTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

Copy constructor.

LPBoundedMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

This is the solution to a bounded linear programming problem.

LPBoundedMinimizer(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer

Constructs the solution for a bounded linear programming problem.

LPCanonicalProblem1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem

This is a linear programming problem in the 1st canonical form (following the convention in the reference): min c'x s.t.

LPCanonicalProblem1(Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1

Construct a linear programming problem in the canonical form.

LPCanonicalProblem1(Vector, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1

Construct a linear programming problem in the canonical form.

LPCanonicalProblem1(LPCanonicalProblem2) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem1

Convert a linear programming problem from the 2nd canonical form to the 1st canonical form.

LPCanonicalProblem2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem

This is a linear programming problem in the 2nd canonical form (following the convention in the wiki): min c'x s.t.

LPCanonicalProblem2(Vector, Matrix, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2

Construct a linear programming problem in the canonical form.

LPCanonicalProblem2(Vector, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2

Construct a linear programming problem in the canonical form.

LPCanonicalProblem2(LPCanonicalProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPCanonicalProblem2

Convert a linear programming problem from the 1st canonical form to the 2nd canonical form.

LPCanonicalSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

This is an LP solver that solves a canonical LP problem in the following form.

LPCanonicalSolver() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPCanonicalSolver

LPDimensionNotMatched - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent.

LPDimensionNotMatched(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPDimensionNotMatched

Construct an instance of LPDimensionNotMatched.

LPEmptyCostVector - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when there is no objective function in a linear programming problem.

LPEmptyCostVector() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPEmptyCostVector

LPException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when there is any problem when solving a linear programming problem.

LPException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPException

Construct an instance of LPException.

LPInfeasible - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when the LP problem is infeasible, i.e., no solution.

LPInfeasible() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPInfeasible

LPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp

An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints.

LPNoConstraint - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when there is no linear constraint found for the LP problem.

LPNoConstraint() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPNoConstraint

LPProblem - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem

A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints.

LPProblemImpl1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem

This is an implementation of a linear programming problem, LPProblem.

LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

Construct a general linear programming problem.

LPProblemImpl1(Vector, LinearGreaterThanConstraints, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

Construct a general linear programming problem with only greater-than-or-equal-to and equality constraints.

LPRevisedSimplexSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

LPRevisedSimplexSolver(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver

LPRevisedSimplexSolver.Problem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

LPRuntimeException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when there is any problem when constructing a linear programming problem.

LPRuntimeException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException

Construct an instance of LPRuntimeException.

LPRuntimeException(String) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPRuntimeException

Construct an instance of LPRuntimeException.

LPSimplexMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

A simplex LP minimizer can be read off from the solution simplex table.

LPSimplexSolution - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

The solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer.

LPSimplexSolver - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table.

LPSolution<T extends LPMinimizer> - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp

A solution to an LP problem contains all information about solving an LP problem such as whether the problem has a solution (bounded), how many minimizers it has, and the minimum.

LPSolver> - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp

An LP solver solves a Linear Programming (LP) problem.

LPStandardProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem

This is a linear programming problem in the standard form: min c'x s.t.

LPStandardProblem(Vector, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPStandardProblem

Construct a linear programming problem in the standard form.

LPTwoPhaseSolver - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver

This implementation solves a linear programming problem, LPProblem, using a two-step approach.

LPTwoPhaseSolver(LPSimplexSolver<LPCanonicalProblem1>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver

Construct an LP solver to solve LP problems.

LPTwoPhaseSolver() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver

Construct an LP solver to solve LP problems.

LPUnbounded - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception

This is the exception thrown when the LP problem is unbounded.

LPUnbounded(int) - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded

Construct an instance of LPUnbounded.

LPUnboundedMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

This is the solution to an unbounded linear programming problem.

LPUnboundedMinimizer(SimplexTable, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

Construct the solution for an unbounded linear programming problem.

LPUnboundedMinimizerScheme2 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution

This is the solution to an unbounded linear programming problem found in scheme 2.

LPUnboundedMinimizerScheme2(SimplexTable, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2

Construct the solution for an unbounded linear programming problem as a result of applying scheme 2.

LSProblem - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

This is the problem of solving a system of linear equations.

LSProblem(Matrix, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Constructs a system of linear equations Ax = b.

Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.Chol

Gets the transpose of the lower triangular matrix, L'.

Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LDLt

Get the transpose of L as in the LDL decomposition.

Lt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies

Gets the transpose of the lower triangular matrix L in the LDL decomposition.

LU - Class in dev.nm.algebra.linear.matrix.doubles.factorization.triangle

LU decomposition decomposes an n x n matrix A so that P * A = L * U.

LU(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU

Run the LU decomposition on a square matrix.

LU(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU

Run the LU decomposition on a square matrix.

LUDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.triangle

LU decomposition decomposes an n x n matrix A so that P * A = L * U.

LUSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

Use LU decomposition to solve Ax = b where A is square and det(A) != 0.

LUSolver() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver

M

m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D

Gets the number of interior x-axis grid points in the solution.

M() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D

Gets the number of interior time-axis grid points in the solution.

m() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the number of interior time-axis grid points in the solution.

m() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

Gets the dimension of the system, i.e., m = the dimension of y.

m() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the number of covariates (number of columns of X), excluding the intercept.

m0() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLM

Get the the mean of x₀.

m0() - Method in class dev.nm.stat.dlm.univariate.DLM

Get the the mean of x₀.

MA() - Method in class dev.nm.stat.evt.timeseries.MARMAModel

Get the MA coefficients.

MA(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the i-th MA coefficient; MA(0) = 1.

MA(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the i-th MA coefficient; MA(0) = 1.

MACH_EPS - Static variable in class dev.nm.misc.Constants

the machine epsilon
This is the difference between 1 and the smallest exactly representable number greater than 1.

MACH_SCALE - Static variable in class dev.nm.misc.Constants

the scale for the machine epsilon

MADecomposition - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess

This class decomposes a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method with symmetric window.

MADecomposition(double[], double[], int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition

Decompose a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method.

MADecomposition(double[], int, int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition

Decompose a time series into the trend, seasonal and stationary random components using the default filter.

MADecomposition(double[], int) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MADecomposition

Decompose a periodic time series into the seasonal and stationary random components using no MA filter.

MAGNETIC_FLUX_QUANTUM_PHI0 - Static variable in class dev.nm.misc.PhysicalConstants

The magnetic flux quantum \(\Phi_0\) in webers (Wb).

MAGNETIC_MU0 - Static variable in class dev.nm.misc.PhysicalConstants

The magnetic constant \(\mu_0\) in henries per meter (H m^-1) or newtons per ampere squared (N A^-2).

main(String[]) - Static method in class dev.nm.misc.license.FloatingLicenseServer

MAModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma

This class represents a univariate MA model.

MAModel(double, double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel

Construct a univariate MA model.

MAModel(double, double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel

Construct a univariate MA model with unit variance.

MAModel(double[], double) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel

Construct a univariate MA model with zero-mean.

MAModel(double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel

Construct a univariate MA model with unit variance and zero-mean.

MAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.MAModel

Copy constructor.

mapClusterIndices(List<List<Integer>>) - Static method in class tech.nmfin.meanreversion.cointegration.PairingModelUtils

marginalInverseTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

Inverse of marginal transform.

marginalTransform(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

Transform to exponential margins under the GEV model.

MarketImpact1 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Constructs the constraint coefficient arrays of a market impact term in the compact form.

MarketImpact1(Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.MarketImpact1

Constructs a market impact term.

MarkowitzByCLM - Class in tech.nmfin.portfoliooptimization.markowitz

Solves for the optimal weights in the Markowitz formulation by critical line method.

MarkowitzByCLM(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM

Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ 0.

MarkowitzByCLM(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM

Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ w_lower, w ≤ w_upper.

MarkowitzByCLM(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByCLM

Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.

MarkowitzByQP - Class in tech.nmfin.portfoliooptimization.markowitz

Modern portfolio theory (MPT) is a theory of investment which attempts to maximize portfolio expected return for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets.

MarkowitzByQP(Vector, Matrix, QPConstraint) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Constructs a Markowitz portfolio from expected future returns and future covariance, assuming zero benchmark rate for Sharpe ratio calculation.

MarkowitzByQP(Vector, Matrix, QPConstraint, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Constructs a Markowitz portfolio from expected future returns and future covariance.

MarkowitzByQP(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Constructs a Markowitz portfolio from expected future returns and future covariance, assuming no short selling constraint and zero benchmark rate.

MarkowitzByQP(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Constructs a Markowitz portfolio from expected future returns and future covariance, with lower and upper limits on asset weights, assuming zero benchmark rate.

MarkowitzByQP(Vector, Matrix, Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.

MarkowitzCriticalLine - Interface in tech.nmfin.portfoliooptimization.clm

MARMAModel - Class in dev.nm.stat.evt.timeseries

Simulation of max autoregressive moving average processes, i.e., MARMA(p, q) processes.

MARMAModel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel

Create an instance with a given GEV distribution for generating innovations.

MARMAModel(double[], double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel

Create an instance with the AR and MA coefficients, using FrechetDistribution as the GEV distribution.

MARMAModel(double[], double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARMAModel

Create an instance with the AR and MA coefficients, and a GEV distribution for generating innovations.

MARMASim - Class in dev.nm.stat.evt.timeseries

Generate random numbers based on a given MARMA model.

MARMASim(MARMAModel) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim

Create an instance with the given MARMAModel.

MARMASim(MARMAModel, RandomNumberGenerator) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim

Create an instance with the given MARMAModel, but override the innovation generation by the the given generator.

MARMASim(MARMAModel, RandomNumberGenerator, double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARMASim

Create an instance with the given MARMAModel and initial values, but override the innovation generation by the the given generator.

MARModel - Class in dev.nm.stat.evt.timeseries

This is equivalent to MARMA(p, 0).

MARModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MARModel

Create an instance with the AR coefficients, using FrechetDistribution as the GEV distribution.

MARModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MARModel

Create an instance with the AR coefficients, and a GEV distribution for generating innovations.

MarsagliaBray1964 - Class in dev.nm.stat.random.rng.univariate.normal

The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables.

MarsagliaBray1964(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964

Construct a random number generator to sample from the standard Normal distribution.

MarsagliaBray1964() - Constructor for class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964

Construct a random number generator to sample from the standard Normal distribution.

MarsagliaTsang2000 - Class in dev.nm.stat.random.rng.univariate.gamma

Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate.

MarsagliaTsang2000(double, double, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000

Construct a random number generator to sample from the gamma distribution.

MarsagliaTsang2000(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000

Construct a random number generator to sample from the gamma distribution.

MarsagliaTsang2000() - Constructor for class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000

Construct a random number generator to sample from the standard gamma distribution.

Mass(X, double) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass

Creates an instance with an outcome and its associated probability.

MAT - Class in dev.nm.algebra.linear.matrix.doubles.operation

MAT is the inverse operator of SVEC.

MAT(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MAT

Constructs the MAT of a vector.

MathTable - Class in dev.nm.misc.datastructure

A mathematical table consists of numbers showing the results of calculation with varying arguments.

MathTable(String...) - Constructor for class dev.nm.misc.datastructure.MathTable

Constructs an empty table by headers.

MathTable(int) - Constructor for class dev.nm.misc.datastructure.MathTable

Constructs an empty table.

MathTable.Row - Class in dev.nm.misc.datastructure

A row is indexed by a number and contains multiple values.

Matrix - Interface in dev.nm.algebra.linear.matrix.doubles

This interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition.

MatrixAccess - Interface in dev.nm.algebra.linear.matrix.doubles

This interface defines the methods for accessing entries in a matrix.

MatrixAccessException - Exception in dev.nm.algebra.linear.matrix

This is the runtime exception thrown when trying to access an invalid entry in a matrix, e.g., A[0, 0].

MatrixAccessException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixAccessException

Construct an instance of MatrixAccessException.

MatrixAccessException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixAccessException

Construct an instance of MatrixAccessException with a message.

MatrixCoordinate - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

The location of a matrix entry is specified by a 2D coordinates (i, j), where i and j are the row-index and column-index of the entry respectively.

MatrixCoordinate(int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate

Construct a matrix coordinate specifying an entry location.

MatrixFactory - Class in dev.nm.algebra.linear.matrix.doubles.operation

These are the utility functions to create a new matrix/vector from existing ones.

MatrixMathOperation - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation

This interface defines some standard operations for generic matrices.

MatrixMeasure - Class in dev.nm.algebra.linear.matrix.doubles.operation

A measure, μ, of a matrix, A, is a map from the Matrix space to the Real line.

MatrixMeasure() - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

MatrixMismatchException - Exception in dev.nm.algebra.linear.matrix

This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.

MatrixMismatchException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException

Construct an instance of MatrixMismatchException.

MatrixMismatchException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixMismatchException

Construct an instance of MatrixMismatchException with a message.

MatrixPropertyUtils - Class in dev.nm.algebra.linear.matrix.doubles

These are the boolean operators that take matrices or vectors and check if they satisfy a certain property.

MatrixRing - Interface in dev.nm.algebra.linear.matrix.doubles

A matrix ring is the set of all n × n matrices over an arbitrary Ring R.

MatrixRootByDiagonalization - Class in dev.nm.algebra.linear.matrix.doubles.operation

The square root of a matrix extends the notion of square root from numbers to matrices.

MatrixRootByDiagonalization(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.MatrixRootByDiagonalization

Constructs the square root of a Matrix by diagonalization.

MatrixSingularityException - Exception in dev.nm.algebra.linear.matrix

This is the runtime exception thrown when an operation acts on a singular matrix, e.g., applying LU decomposition to a singular matrix.

MatrixSingularityException() - Constructor for exception dev.nm.algebra.linear.matrix.MatrixSingularityException

Construct an instance of MatrixSingularityException.

MatrixSingularityException(String) - Constructor for exception dev.nm.algebra.linear.matrix.MatrixSingularityException

Construct an instance of MatrixSingularityException with a message.

MatrixTable - Interface in dev.nm.algebra.linear.matrix.doubles

A matrix is represented by a rectangular table structure with accessors.

MatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.operation

These are the utility functions to apply to matrices.

MatthewsDavies - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite

Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.

MatthewsDavies(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.MatthewsDavies

Constructs a symmetric, positive definite matrix using the Matthews-Davies algorithm.

max(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Compute the maximal entry in a matrix.

max(DateTime, DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Return the later of two DateTime instances.

max(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the maximum of the values.

max(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the maximum of the values.

Max - Class in dev.nm.stat.descriptive.rank

The maximum of a sample is the biggest value in the sample.

Max() - Constructor for class dev.nm.stat.descriptive.rank.Max

Construct an empty Max calculator.

Max(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Max

Construct a Max calculator, initialized with a sample.

Max(Max) - Constructor for class dev.nm.stat.descriptive.rank.Max

Copy constructor.

max_abs_cor() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators

Gets the estimated sequence of maximal absolute correlations.

MAX_D - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

MAX_ITERATION - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

MAX_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

MAX_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

maxBinSize() - Method in class dev.nm.misc.algorithm.Bins

Gets the maximal size of the bins.

maxClusterSize() - Method in class dev.nm.graph.community.GirvanNewman

Get the size of the maximal cluster.

maxDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction

Get the biggest abscissae.

maxEdge() - Method in class dev.nm.graph.community.EdgeBetweeness

Gets the edge with the maximal edge-betweeness.

maxEdge() - Method in class dev.nm.graph.community.GirvanNewman

Gets the edge with the maximal edge-betweeness.

MaximaDistribution - Class in dev.nm.stat.evt.evd.univariate

The distribution of \(M\), where \(M=\max(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).

MaximaDistribution(ProbabilityDistribution, int) - Constructor for class dev.nm.stat.evt.evd.univariate.MaximaDistribution

Create an instance with the probability distribution of \(X\), and the number of iid samples to be drawn.

MaximizationSolution<T> - Interface in dev.nm.solver

This is the solution to a maximization problem.

maximizer() - Method in interface dev.nm.solver.MaximizationSolution

Get the maximizer (solution) to the maximization problem.

maximum() - Method in interface dev.nm.solver.MaximizationSolution

Get the (approximate) maximum found.

MaximumLikelihoodFitting - Interface in dev.nm.stat.evt.evd.univariate.fitting

This interface defines model fitting by maximum likelihood algorithm.

maxIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils

Get the index of the maximum of the values, skipping Double.NaN.

maxIndex(double...) - Static method in class dev.nm.number.DoubleUtils

Get the index of the maximum of the values, skipping Double.NaN.

maxIterations - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer

maxIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

maxIterations - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer

the maximum number of iterations

maxIterations - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer

the maximum number of iterations

MaxIterationsExceededException(int) - Constructor for exception dev.nm.analysis.function.rn2r1.univariate.ContinuedFraction.MaxIterationsExceededException

Construct a new MaxIterationsExceededException, indicating the number of iterations.

Maxmizer> - Interface in dev.nm.solver

This interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension.

maxOrder() - Method in class dev.nm.analysis.curvefit.interpolation.univariate.DividedDifferences

Get the maximum order which is limited by the number of points given for the computation.

maxPQ() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the maximum of AR length or MA length.

maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the maximum of AR length or MA length.

maxPQ() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

Get the maximum of the ARCH length or GARCH length.

maxValue() - Method in class dev.nm.graph.community.EdgeBetweeness

Gets the maximum of edge-betweeness-es.

maxValue() - Method in class dev.nm.graph.community.GirvanNewman

Get the maximum of edge-betweeness.

maxZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

McCormickMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

Deprecated.
the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.

McCormickMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.McCormickMinimizer

Deprecated.
the McCormick algorithm does not seem to work well; need further investigation; don't use it. TODO. Use BFGSMinimizer instead.

MCLNiedermayer - Class in tech.nmfin.portfoliooptimization.clm

Implements Markowitz's critical line algorithm.

MCLNiedermayer(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer

Creates the critical line for given gain vector and covariance matrix, with non-negativity constraint.

MCLNiedermayer(Vector, Matrix, Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer

Creates the critical line for given gain vector and covariance matrix, with given lower and upper bounds for weights.

MCUtils - Class in dev.nm.stat.markovchain

These are the utility functions to examine a Markov chain.

mean() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

Get the sample mean.

Mean - Class in dev.nm.stat.descriptive.moment

The mean of a sample is the sum of all numbers in the sample, divided by the sample size.

Mean() - Constructor for class dev.nm.stat.descriptive.moment.Mean

Construct an empty Mean calculator.

Mean(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Mean

Construct a Mean calculator, initialized with a sample.

Mean(Mean) - Constructor for class dev.nm.stat.descriptive.moment.Mean

Copy constructor.

mean() - Method in class dev.nm.stat.descriptive.moment.Skewness

Get the sample mean.

mean() - Method in class dev.nm.stat.descriptive.moment.Variance

Get the sample mean.

mean() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution

mean() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution

mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution

mean() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution

Gets the mean of this distribution.

mean() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.FDistribution

Gets the mean of this distribution.

mean() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

mean() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the mean of this distribution.

mean() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.TDistribution

Gets the mean of this distribution.

mean() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

mean() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

mean() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD

mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

mean() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

\[ \mu + \frac{\sigma}{1-\xi} \] for \(\xi < 1\).

mean() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

mean() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

mean() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

mean() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the sample means that were subtracted.

mean() - Method in class dev.nm.stat.factor.pca.PCAbySVD

mean() - Method in interface dev.nm.stat.random.Estimator

Gets the expectation of the estimator.

mean() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation

Compute the mean of the integral.

mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

mean() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

mean() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

mean() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

mean() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

Computes the sample mean of the in-sample spread.

mean() - Method in interface tech.nmfin.meanreversion.volarb.MeanEstimator

mean() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift

mean1() - Method in class dev.nm.stat.test.mean.T

Get the mean of the first sample.

mean2() - Method in class dev.nm.stat.test.mean.T

Get the mean of the second sample.

MeanEstimator - Interface in dev.nm.stat.random

MeanEstimator - Interface in tech.nmfin.meanreversion.volarb

Defines how to estimate the mean price.

MeanEstimatorMaxLevelShift - Class in tech.nmfin.meanreversion.volarb

MeanEstimatorMaxLevelShift(int, double, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift

MeanEstimatorMaxLevelShift(int, double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift

median() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

median() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

Gets the median of this distribution.

median() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

median() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

median() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

median() - Method in class dev.nm.stat.distribution.univariate.FDistribution

Deprecated.
Not supported yet.

median() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

median() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

median() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

median() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

median() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the median of this distribution.

median() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

median() - Method in class dev.nm.stat.distribution.univariate.TDistribution

median() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

median() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

median() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

median() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

\[ \mu + \frac{\sigma( 2^{\xi} -1)}{\xi} \]

median() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

median() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

median() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

median() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

median() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Deprecated.

median() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Deprecated.

mergeIntervals(RealInterval[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.EigenBoundUtils

MersenneExponent - Enum in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation

The value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister random number generator with a period of 2^p.

MersenneTwister - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister

Mersenne Twister is one of the best pseudo random number generators available.

MersenneTwister() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

Constructs a random number generator to sample uniformly from [0, 1).

MersenneTwister(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

Constructs a random number generator to sample uniformly from [0, 1).

MersenneTwister(MersenneTwisterParam) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

Constructs a new instance, which uses parameters and the state contained in the given MersenneTwisterParam instance.

MersenneTwister(MersenneTwisterParam, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

MersenneTwisterParam - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister

Immutable parameters for creating a MersenneTwister RNG.

MersenneTwisterParam() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam

MersenneTwisterParam(int, int, int, int, int, int, int, int, int, int, int, int, int, long, long) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwisterParam

MersenneTwisterParamSearcher - Class in dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation

Searches for Mersenne-Twister parameters.

MersenneTwisterParamSearcher(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher

Constructs a new instance which uses the given RNG to do the parameter search.

MersenneTwisterParamSearcher(RandomLongGenerator, MersenneExponent) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher

Constructs a new instance which uses the given RNG to do the parameter search, with the given period parameter.

Metropolis - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis

This basic Metropolis implementation assumes using symmetric proposal function.

Metropolis(RealScalarFunction, RealVectorFunction, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis

Constructs a new instance with the given parameters.

Metropolis(RealScalarFunction, Vector, double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis

Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, with the given variance and zero covariance.

Metropolis(RealScalarFunction, Vector, Matrix, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis

Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, multiplied by the given scale matrix.

MetropolisAcceptanceProbabilityFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction

Uses the classic Metropolis rule, f_{t+1}/f_t.

MetropolisAcceptanceProbabilityFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction.MetropolisAcceptanceProbabilityFunction

MetropolisHastings - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis

A generalization of the Metropolis algorithm, which allows asymmetric proposal functions.

MetropolisHastings(RealScalarFunction, ProposalFunction, MetropolisHastings.ProposalDensityFunction, Vector, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings

Constructs a new instance with the given parameters.

MetropolisHastings.ProposalDensityFunction - Interface in dev.nm.stat.random.rng.multivariate.mcmc.metropolis

Defines the density of a proposal function, i.e.

MetropolisUtils - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis

Utility functions for Metropolis algorithms.

Midpoint - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes

The midpoint rule computes an approximation to a definite integral, made by finding the area of a collection of rectangles whose heights are determined by the values of the function.

Midpoint(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Midpoint

Construct an integrator that implements the Midpoint rule.

MilsteinSDE - Class in dev.nm.stat.stochasticprocess.univariate.sde.discrete

Milstein scheme is a first-order approximation to a continuous-time SDE.

MilsteinSDE(SDE) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.discrete.MilsteinSDE

Discretize a continuous-time SDE using the Milstein scheme.

min(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Compute the minimal entry in a matrix.

min(DateTime, DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Return the earlier of two DateTime instances.

min(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the minimum of the values.

min() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution

Min - Class in dev.nm.stat.descriptive.rank

The minimum of a sample is the smallest value in the sample.

Min() - Constructor for class dev.nm.stat.descriptive.rank.Min

Construct an empty Min calculator.

Min(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Min

Construct a Min calculator, initialized with a sample.

Min(Min) - Constructor for class dev.nm.stat.descriptive.rank.Min

Copy constructor.

MIN_P - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

MIN_Q - Static variable in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

minDomain() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction

Get the smallest abscissae.

MinimaDistribution - Class in dev.nm.stat.evt.evd.univariate

The distribution of \(M\), where \(M=\min(x_1,x_2,...,x_n)\) and \(x_i\)'s are iid samples drawn from of a random variable \(X\) with cdf \(F(x)\).

MinimaDistribution(ProbabilityDistribution, int) - Constructor for class dev.nm.stat.evt.evd.univariate.MinimaDistribution

MinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary

The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular).

MinimalResidualSolver(PreconditionerFactory, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver

Construct a MINRES solver.

MinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver

Construct a MINRES solver.

MinimizationSolution<T> - Interface in dev.nm.solver

This is the solution to a minimization problem.

minimizer() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound

minimizer() - Method in interface dev.nm.solver.MinimizationSolution

Get the minimizer (solution) to the minimization problem.

Minimizer> - Interface in dev.nm.solver

This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension.

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

This is the same as the u vector, such that the direction of arbitrarily negative can be computed by adjusting λ.

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2

minimizer() - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSolution

Get a minimizing vector.

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution

minimizer() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

minimizer() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

minimizer() - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution

minimizers() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer

Get all optimal minimizers.

minimum() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound

minimum() - Method in interface dev.nm.solver.MinimizationSolution

Get the (approximate) minimum found.

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

Get the (approximate) minimum found.

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution

Deprecated.

minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution

minimum() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

minimum() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

minimum() - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution

MinimumWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint

This constraint puts lower bounds on weights.

MinimumWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.MinimumWeights

minIndex(boolean, int, int, double...) - Static method in class dev.nm.number.DoubleUtils

Get the index of the minimum of the values, skipping Double.NaN.

minIndex(double...) - Static method in class dev.nm.number.DoubleUtils

Get the index of the minimum of the values, skipping Double.NaN.

MinMaxMinimizer<T> - Interface in dev.nm.solver.multivariate.minmax

A minmax minimizer minimizes a minmax problem.

MinMaxProblem<T> - Interface in dev.nm.solver.multivariate.minmax

A minmax problem is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain.

minorMatrix(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Gets the minor matrix of a given matrix, by removing a specified row and a specified column.

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

minus(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

this - that

minus(DenseData) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

Subtract the elements in this by that, element-by-element.

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

Computes the difference between two diagonal matrices.

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

minus(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation

A₁ - A₂

minus(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

minus(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

minus(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

minus(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

minus(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

minus(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

minus(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

minus(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

minus(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

minus(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

A vector subtracts another vector, element-by-element.

minus(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Subtracts a constant from a vector, element-by-element.

minus(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

minus(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

minus(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

\(this - that\)

minus(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Subtract a constant from all entries in this vector.

minus(G) - Method in interface dev.nm.algebra.structure.AbelianGroup

- : G × G → G
The operation "-" is not in the definition of of an additive group but can be deduced.

minus(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial

minus(Complex) - Method in class dev.nm.number.complex.Complex

minus(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation

minus(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation

Subtract one double array from another, entry-by-entry.

minus(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation

minus(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation

minus(Real) - Method in class dev.nm.number.Real

minusWeekdayPeriod(DateTime, Period) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Subtract a weekday-period (i.e., skipping weekends) from a DateTime.

minZ() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

MixedRule - Class in dev.nm.analysis.integration.univariate.riemann.substitution

The mixed rule is good for functions that fall off rapidly at infinity, e.g., \(e^{x^2}\) or \(e^x\) The integral region is \((0, +\infty)\).

MixedRule(UnivariateRealFunction, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.MixedRule

Construct a MixedRule substitution rule.

MixtureDistribution - Interface in dev.nm.stat.hmm.mixture.distribution

This is the conditional distribution of the observations in each state (possibly differently parameterized) of a mixture hidden Markov model.

MixtureHMM - Class in dev.nm.stat.hmm.mixture

This is the mixture hidden Markov model (HMM).

MixtureHMM(Vector, Matrix, MixtureDistribution) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM

Constructs a mixture hidden Markov model.

MixtureHMM(MixtureHMM) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMM

Copy constructor.

MixtureHMMEM - Class in dev.nm.stat.hmm.mixture

The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm.

MixtureHMMEM(double[], MixtureHMM, double, int) - Constructor for class dev.nm.stat.hmm.mixture.MixtureHMMEM

Constructs a mixture HMM model by training an initial model using the Baum-Welch algorithm.

MixtureHMMEM.TrainedModel - Class in dev.nm.stat.hmm.mixture

the result of the EM algorithm

ML() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression

Gets the maximum log-likelihood.

MMAModel - Class in dev.nm.stat.evt.timeseries

This is equivalent to MARMA(0, q).

MMAModel(double[]) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel

Create an instance with the MA coefficients, using FrechetDistribution as the GEV distribution.

MMAModel(double[], UnivariateEVD) - Constructor for class dev.nm.stat.evt.timeseries.MMAModel

Create an instance with the MA coefficients, and a GEV distribution for generating innovations.

mod(long, long) - Static method in class dev.nm.analysis.function.FunctionOps

Compute the positive modulus of a number.

mode() - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution

mode() - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution

mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution

mode() - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution

Gets the mode of this distribution.

mode() - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution

mode() - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD

model - Variable in class dev.nm.stat.hmm.mixture.MixtureHMMEM.TrainedModel

the newly trained model as a result of the EM algorithm

ModelNotFound(String) - Constructor for exception dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection.ModelNotFound

Construct a ModelNotFound exception with an error message.

ModelParam(double, double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

ModelParam(double, double, double, double, double) - Constructor for class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

ModelResamplerFactory - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler

ModelResamplerFactory(RandomLongGenerator) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory

ModelResamplerFactory() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory

modpow(long, long, long) - Static method in class dev.nm.analysis.function.FunctionOps

b^e mod m

modulus() - Method in class dev.nm.number.complex.Complex

Get the modulus.

modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator

modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

modulus() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator

Get the modulus of this linear congruential generator.

modulus() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

MOLAR_GAS_R - Static variable in class dev.nm.misc.PhysicalConstants

The molar gas constant \(R\) in joule per kelvin mole (J mol^-1 K^-1).

moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.DirichletDistribution

moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultinomialDistribution

moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution

moment(Vector) - Method in interface dev.nm.stat.distribution.multivariate.MultivariateProbabilityDistribution

The moment generating function is the expected value of e^tX.

moment(Vector) - Method in class dev.nm.stat.distribution.multivariate.MultivariateTDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

Deprecated.
Not supported yet.

moment(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

Deprecated.
Not supported yet.

moment(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

moment(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

The moment generating function is the expected value of e^tX.

moment(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

moment(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

moment(Vector) - Method in class dev.nm.stat.evt.evd.bivariate.AbstractBivariateEVD

moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

moment(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

moment(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

moment(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

moment(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

moment(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Deprecated.

moment(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Deprecated.

moment1() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments

moment2() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments

Moments - Class in dev.nm.stat.descriptive.moment

Compute the central moment of a data set incrementally.

Moments(int) - Constructor for class dev.nm.stat.descriptive.moment.Moments

Construct an empty moment calculator, computing all moments up to and including the order-th moment.

Moments(int, double...) - Constructor for class dev.nm.stat.descriptive.moment.Moments

Construct a moment calculator, computing all moments up to and including the order-th moment.

Moments(Moments) - Constructor for class dev.nm.stat.descriptive.moment.Moments

Copy constructor.

moments() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights

MomentsEstimatorLedoitWolf - Class in tech.nmfin.returns.moments

Estimates the first two moments of returns using Ledoit-Wolf 2004.

MomentsEstimatorLedoitWolf() - Constructor for class tech.nmfin.returns.moments.MomentsEstimatorLedoitWolf

Monoid<G> - Interface in dev.nm.algebra.structure

A monoid is a group with a binary operation (×), satisfying the group axioms: closure associativity existence of multiplicative identity

moveColumn2End(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Swaps a column of a permutation matrix with the last column.

moveRow2End(int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Swaps a row of the permutation matrix with the last row.

MovingAverage - Class in dev.nm.dsp.univariate.operation.system.doubles

This applies a linear filter to a univariate time series using the moving average estimation.

MovingAverage(double[], MovingAverage.Side) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage

Construct a moving average filter.

MovingAverage(double[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage

Construct a moving average filter using a symmetric window.

MovingAverage.Side - Enum in dev.nm.dsp.univariate.operation.system.doubles

the available types of moving average filtering

MovingAverageByExtension - Class in dev.nm.dsp.univariate.operation.system.doubles

This implements a moving average filter with these properties: 1) both past and future observations are used in smoothing; 2) the head is prepended with the first element in the inputs (x_t = x_1 for t < 1); 3) the tail is appended with the last element in the inputs (x_t = x_n for t > n).

MovingAverageByExtension(double[]) - Constructor for class dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension

Construct a moving average filter with prepending and appending.

MR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR).

MR3(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3

Creates an instance for computing eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T.

MR3(Vector, Vector, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3

Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T.

MR3(Vector, Vector, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.MR3

Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T.

MRG - Class in dev.nm.stat.random.rng.univariate.uniform.linear

A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form:

MRG(long, long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

Construct a Multiple Recursive Generator.

MRModel - Interface in tech.nmfin.meanreversion.volarb

A Mean Reversion Model computes the target position given the current price.

MRModelRanged - Class in tech.nmfin.meanreversion.volarb

MRModelRanged(double, double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged

MRModelRanged(double) - Constructor for class tech.nmfin.meanreversion.volarb.MRModelRanged

mu(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D

Gets the convection coefficient at a given time t and a position x.

mu - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda

the mean

mu() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting

Gets μ as in E(Y) = μ = g^-1(Xβ)

mu() - Method in class dev.nm.stat.regression.linear.glm.IWLS

mu() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson

mu() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE

Get the drift: \(\mu(t,X_t,Z_t,...)\).

mu() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

mu() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess

Get the overall mean.

mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the intercept (constant) vector.

mu() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Get the intercept vector.

mu() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the intercept (constant) term.

mu() - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Gets the long term mean.

mu() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint

mu() - Method in class tech.nmfin.returns.moments.ReturnsMoments

Gets the mean vector.

mu - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

mu1 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

mu1() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the drift in the bull market.

mu2 - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

mu2() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the drift in the bear market.

MultiCubicSpline - Class in dev.nm.analysis.curvefit.interpolation.multivariate

Implementation of natural cubic spline interpolation for an arbitrary number of dimensions.

MultiCubicSpline() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiCubicSpline

Create an instance with CubicSpline as the implementation of the univariate cubic spline interpolation algorithm.

MultiDimensionalArray<T> - Class in dev.nm.misc.datastructure

A generic multi-dimensional array, with an arbitrary number of dimensions.

MultiDimensionalArray(int...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray

Creates an instance with the specified size along each dimension.

MultiDimensionalArray(MultiDimensionalCollection<T>) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalArray

A copy constructor that constructs a shallow copy of the given collection of instances.

MultiDimensionalCollection<T> - Interface in dev.nm.misc.datastructure

A generic collection with an arbitrary number of dimensions.

MultiDimensionalGrid - Class in dev.nm.misc.datastructure

An arbitrary dimensional grid.

MultiDimensionalGrid(Double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid

Constructs a multi-dimensional grid of points.

MultiDimensionalGrid(double[]...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid

Constructs a multi-dimensional grid of points.

MultiDimensionalGrid(MultiDimensionalGrid.Discretization...) - Constructor for class dev.nm.misc.datastructure.MultiDimensionalGrid

Constructs a multi-dimensional grid of points.

MultiDimensionalGrid.Discretization - Class in dev.nm.misc.datastructure

Specifies the discretization of an interval.

MultiLinearInterpolation - Class in dev.nm.analysis.curvefit.interpolation.multivariate

Implementation of linear interpolation for an arbitrary number of dimensions.

MultiLinearInterpolation() - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultiLinearInterpolation

Create an instance with LinearInterpolation as the implementation of the univariate linear interpolation algorithm.

MultinomialBetaFunction - Class in dev.nm.analysis.function.special.beta

A multinomial Beta function is defined as: \[ \frac{\prod_{i=1}^K \Gamma(\alpha_i)}{\Gamma\left(\sum_{i=1}^K \alpha_i\right)},\qquad\boldsymbol{\alpha}=(\alpha_1,\cdots,\alpha_K) \]

MultinomialBetaFunction(int) - Constructor for class dev.nm.analysis.function.special.beta.MultinomialBetaFunction

Constructs an instance of a multinomial Beta function.

MultinomialDistribution - Class in dev.nm.stat.distribution.multivariate

MultinomialDistribution(int, double...) - Constructor for class dev.nm.stat.distribution.multivariate.MultinomialDistribution

Constructs an instance of a Multinomial distribution.

MultinomialRVG - Class in dev.nm.stat.random.rng.multivariate

A multinomial distribution puts N objects into K bins according to the bins' probabilities.

MultinomialRVG(int, double[], RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG

Constructs a multinomial random vector generator.

MultinomialRVG(int, double[]) - Constructor for class dev.nm.stat.random.rng.multivariate.MultinomialRVG

Constructs a multinomial random vector generator.

MultipleExecutionException - Exception in dev.nm.misc.parallel

This exception is thrown when any of the parallel tasks throws an exception during execution.

MultipleExecutionException(List<?>, List<ExecutionException>) - Constructor for exception dev.nm.misc.parallel.MultipleExecutionException

Construct an exception with the (partial) results and all exceptions encountered during execution.

MultiplicativeModel - Class in dev.nm.stat.timeseries.linear.univariate.stationaryprocess

The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components.

MultiplicativeModel(double[], double[], double[]) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel

Construct a univariate time series by multiplying the components.

MultiplicativeModel(double[], double[], RandomNumberGenerator) - Constructor for class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.MultiplicativeModel

Construct a univariate time series by multiplying the components.

MultiplierPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod

A multiplier penalty function allows different weights to be assigned to the constraints.

MultiplierPenalty(Constraints, double[]) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty

Construct a multiplier penalty function from a collection of constraints.

MultiplierPenalty(Constraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty

Construct a multiplier penalty function from a collection of constraints.

MultiplierPenalty(Constraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty

Construct a multiplier penalty function from a collection of constraints.

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

multiply(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix

Right multiply this matrix, A, by a vector.

multiply(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

this * that

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

this * that

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

Computes the product of two diagonal matrices.

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Left multiplication by G, namely, G * A.

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

multiply(MatrixAccess, MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation

A₁ * A₂

multiply(MatrixAccess, Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation

A * v

multiply(double[], double[], double[], int, int, int) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplication

Multiplies two matrices, C = A %*% B.

multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock

multiply(double[], double[], double[], int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByIjk

multiply(MatrixAccess, MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

multiply(MatrixAccess, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Left multiplication by P.

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Left multiplication by P.

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

multiply(SparseVector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

multiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

multiply(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

multiply(ComplexMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

multiply(GenericFieldMatrix<F>) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

multiply(RealMatrix) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

multiply(DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

multiply(Vector, Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Multiplies two vectors, element-by-element.

multiply(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

multiply(Vector) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Multiply this by that, entry-by-entry.

multiply(G) - Method in interface dev.nm.algebra.structure.Monoid

× : G × G → G

multiply(Polynomial) - Method in class dev.nm.analysis.function.polynomial.Polynomial

multiply(Complex) - Method in class dev.nm.number.complex.Complex

Compute the product of this complex number and that complex number.

multiply(double[], double[]) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation

multiply(double[], double[]) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation

Multiply one double array to another, entry-by-entry.

multiply(double[], double[]) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation

multiply(double[], double[]) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation

multiply(Real) - Method in class dev.nm.number.Real

multiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Left multiplication by G, namely, G * A.

multiplyInPlace(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Right multiplies this matrix, A, by a vector.

MultipointHybridMCMC - Class in dev.nm.stat.random.rng.multivariate.mcmc.hybrid

A multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamics simulation, we pick a proposal from a window of the last M configurations.

MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC

Constructs a new instance with the given parameters.

MultipointHybridMCMC(RealScalarFunction, RealVectorFunction, Vector, double, int, int, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC

Constructs a new instance with equal weights to the M configurations.

MultivariateArrayGrid - Class in dev.nm.analysis.curvefit.interpolation.multivariate

Implementation of MultivariateGrid, backed by the given MultiDimensionalCollection instance.

MultivariateArrayGrid(MultiDimensionalCollection<Double>, double[]...) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid

Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.

MultivariateAutoCorrelationFunction - Class in dev.nm.stat.timeseries.linear.multivariate

This is the auto-correlation function of a multi-dimensional time series {Xt}.

MultivariateAutoCorrelationFunction() - Constructor for class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction

MultivariateAutoCovarianceFunction - Class in dev.nm.stat.timeseries.linear.multivariate

This is the auto-covariance function of a multi-dimensional time series {X_t}, \[ K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)') \] For a stationary process, the auto-covariance depends only on the lag, |i - j|.

MultivariateAutoCovarianceFunction() - Constructor for class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCovarianceFunction

MultivariateBrownianRRG - Class in dev.nm.stat.stochasticprocess.multivariate.random

This is the Random Walk construction of a multivariate Brownian motion.

MultivariateBrownianRRG(int, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG

Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.

MultivariateBrownianRRG(int, TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG

Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.

MultivariateBrownianRRG(int, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG

Construct a random realization generator to produce multi-dimensional Brownian paths at evenly spaced time points [0, 1, ...].

MultivariateBrownianSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde.discrete

A multivariate Brownian motion is a stochastic process with the following properties.

MultivariateBrownianSDE(int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE

Construct a standard multi-dimensional Brownian motion.

MultivariateBrownianSDE(Vector, Matrix) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE

Construct a multi-dimensional Brownian motion.

MultivariateDiscreteSDE - Interface in dev.nm.stat.stochasticprocess.multivariate.sde.discrete

This interface represents the discrete approximation of a multivariate SDE.

MultivariateDLM - Class in dev.nm.stat.dlm.multivariate

This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification.

MultivariateDLM(Vector, Matrix, MultivariateObservationEquation, MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM

Construct a (multivariate) controlled dynamic linear model.

MultivariateDLM(MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLM

Copy constructor.

MultivariateDLMSeries - Class in dev.nm.stat.dlm.multivariate

This is a simulator for a multivariate controlled dynamic linear model process.

MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries

Simulate a multivariate controlled dynamic linear model process.

MultivariateDLMSeries(int, MultivariateDLM, MultivariateIntTimeTimeSeries) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries

Simulate a multivariate controlled dynamic linear model process.

MultivariateDLMSeries(int, MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries

Simulate a multivariate controlled dynamic linear model process.

MultivariateDLMSeries.Entry - Class in dev.nm.stat.dlm.multivariate

This is the TimeSeries.Entry for a multivariate DLM time series.

MultivariateDLMSim - Class in dev.nm.stat.dlm.multivariate

This is a simulator for a multivariate controlled dynamic linear model process.

MultivariateDLMSim(MultivariateDLM, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateDLMSim

Simulates a multivariate controlled dynamic linear model process.

MultivariateDLMSim.Innovation - Class in dev.nm.stat.dlm.multivariate

a simulated innovation

MultivariateEulerSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde.discrete

The Euler scheme is the first order approximation of an SDE.

MultivariateEulerSDE(MultivariateSDE) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE

Discretize a multivariate, continuous-time SDE using the Euler scheme.

MultivariateExponentialFamily - Class in dev.nm.stat.distribution.multivariate.exponentialfamily

The exponential family is an important class of probability distributions sharing this particular form.

MultivariateExponentialFamily(RealScalarFunction, RealVectorFunction, RealVectorFunction, RealScalarFunction) - Constructor for class dev.nm.stat.distribution.multivariate.exponentialfamily.MultivariateExponentialFamily

Construct a factory to construct probability distribution in the exponential family of this form.

MultivariateFiniteDifference - Class in dev.nm.analysis.differentiation.multivariate

A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant.

MultivariateFiniteDifference(RealScalarFunction, int[]) - Constructor for class dev.nm.analysis.differentiation.multivariate.MultivariateFiniteDifference

Construct the partial derivative of a multi-variable function.

MultivariateForecastOneStep - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess

The innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time series with known auto-covariance and these properties (not limited to ARMA processes): {x_t} can be non-stationary. E(x_t) = 0 for all t.

MultivariateForecastOneStep(MultivariateIntTimeTimeSeries, MultivariateAutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep

Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.

MultivariateFt - Class in dev.nm.stat.stochasticprocess.multivariate.sde

This represents the concept 'Filtration', the information available at time t.

MultivariateFt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Construct an empty filtration (no information).

MultivariateFt(MultivariateFt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Copy constructor.

MultivariateFtWt - Class in dev.nm.stat.stochasticprocess.multivariate.sde

This is a filtration implementation that includes the path-dependent information, W_t.

MultivariateFtWt() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

Construct an empty filtration (no information).

MultivariateFtWt(MultivariateFtWt) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

Copy constructor.

MultivariateGenericTimeTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.multivariate

This is a multivariate time series indexed by some notion of time.

MultivariateGenericTimeTimeSeries(T[], Vector[]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Construct a multivariate time series from timestamps and vectors.

MultivariateGenericTimeTimeSeries(T[], double[][]) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Construct a multivariate time series from timestamps and vectors.

MultivariateGenericTimeTimeSeries(T[], Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Construct a multivariate time series from timestamps and vectors.

MultivariateGrid - Interface in dev.nm.analysis.curvefit.interpolation.multivariate

A multivariate rectilinear (not necessarily uniform) grid of double values.

MultivariateGridInterpolation - Interface in dev.nm.analysis.curvefit.interpolation.multivariate

Interpolation on a rectilinear multi-dimensional grid.

MultivariateInnovationAlgorithm - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess

This class implements the part of the innovation algorithm that computes the prediction error covariances, V and prediction coefficients Θ.

MultivariateInnovationAlgorithm(int, MultivariateAutoCovarianceFunction) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm

Run the Innovation Algorithm to compute the prediction parameters, V and Θ.

MultivariateIntTimeTimeSeries - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime

This is a multivariate time series indexed by integers.

MultivariateIntTimeTimeSeries.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime

This is the TimeSeries.Entry for an integer -indexed multivariate time series.

MultivariateLinearKalmanFilter - Class in dev.nm.stat.dlm.multivariate

The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those that would be based on a single measurement alone.

MultivariateLinearKalmanFilter(MultivariateDLM) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter

Construct a Kalman filter from a multivariate controlled dynamic linear model.

MultivariateMinimizer> - Interface in dev.nm.solver.multivariate.unconstrained

This is a minimizer that minimizes a multivariate function or a Vector function.

MultivariateNormalDistribution - Class in dev.nm.stat.distribution.multivariate

The multivariate Normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) Normal distribution to higher dimensions.

MultivariateNormalDistribution(Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution

Constructs an instance with the given mean and covariance matrix.

MultivariateNormalDistribution(int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateNormalDistribution

Constructs an instance of the standard Normal distribution.

MultivariateObservationEquation - Class in dev.nm.stat.dlm.multivariate

This is the observation equation in a controlled dynamic linear model.

MultivariateObservationEquation(R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Constructs an observation equation.

MultivariateObservationEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Constructs an observation equation.

MultivariateObservationEquation(Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Constructs a time-invariant an observation equation.

MultivariateObservationEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Constructs a time-invariant an observation equation.

MultivariateObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Constructs a multivariate observation equation from a univariate observation equation.

MultivariateObservationEquation(MultivariateObservationEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Copy constructor.

MultivariateProbabilityDistribution - Interface in dev.nm.stat.distribution.multivariate

A multivariate or joint probability distribution for X, Y, ... is a probability distribution that gives the probability that each of X, Y, ... falls in any particular range or discrete set of values specified for that variable.

MultivariateRandomProcess - Class in dev.nm.stat.stochasticprocess.multivariate.random

This interface represents a multivariate random process a.k.a.

MultivariateRandomProcess(int, TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

Construct a multivariate random process.

MultivariateRandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.multivariate.random

This interface defines a generator to construct random realizations from a multivariate stochastic process.

MultivariateRandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.multivariate.random

This class generates random realizations from a multivariate random/stochastic process.

MultivariateRandomRealizationOfRandomProcess(MultivariateRandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

Construct a random realization generator from a multivariate random/stochastic process.

MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

Construct a random realization generator from a multivariate discrete SDE.

MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

Construct a random realization generator from a multivariate SDE.

MultivariateRandomRealizationOfRandomProcess(MultivariateSDE, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

Construct a random realization generator from a multivariate SDE.

MultivariateRandomWalk - Class in dev.nm.stat.stochasticprocess.multivariate.random

This is the Random Walk construction of a multivariate stochastic process per SDE specification.

MultivariateRandomWalk(MultivariateDiscreteSDE, TimeGrid, Vector) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk

Construct a multivariate stochastic process from an SDE.

MultivariateRealization - Interface in dev.nm.stat.timeseries.datastructure.multivariate.realtime

A multivariate realization is a multivariate time series indexed by real numbers, e.g., real time.

MultivariateRealization.Entry - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime

This is the TimeSeries.Entry for a real number -indexed multivariate time series.

MultivariateRegularGrid - Class in dev.nm.analysis.curvefit.interpolation.multivariate

A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e.g.

MultivariateRegularGrid(MultiDimensionalCollection<Double>, MultivariateRegularGrid.EquallySpacedVariable...) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

Create a new instance where the dependent variable is specified by a MultiDimensionalCollection and the independent variables form the specified grid.

MultivariateRegularGrid.EquallySpacedVariable - Class in dev.nm.analysis.curvefit.interpolation.multivariate

Specify the positioning and spacing along one dimension.

MultivariateResampler - Interface in dev.nm.stat.random.sampler.resampler.multivariate

This is the interface of a multivariate re-sampler method.

MultivariateSDE - Class in dev.nm.stat.stochasticprocess.multivariate.sde

This class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form: \[ dX_t = \mu(t,X_t,Z_t,...)*dt + \sigma(t, X_t, Z_t, ...)*dB_t \]

MultivariateSDE(DriftVector, DiffusionMatrix, int) - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE

Construct a multi-dimensional diffusion type stochastic differential equation.

MultivariateSimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime

This simple multivariate time series has its vectored values indexed by integers.

MultivariateSimpleTimeSeries(Matrix) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries.

MultivariateSimpleTimeSeries(double[]...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries.

MultivariateSimpleTimeSeries(Vector...) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries.

MultivariateSimpleTimeSeries(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

Construct an instance of MultivariateSimpleTimeSeries from a univariate time series.

MultivariateStateEquation - Class in dev.nm.stat.dlm.multivariate

This is the state equation in a controlled dynamic linear model.

MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a state equation.

MultivariateStateEquation(R1toMatrix, R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a state equation.

MultivariateStateEquation(R1toMatrix, R1toMatrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a state equation without control variables.

MultivariateStateEquation(Matrix, Matrix, Matrix, NormalRVG) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a time-invariant state equation.

MultivariateStateEquation(Matrix, Matrix) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a time-invariant state equation without control variables.

MultivariateStateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Constructs a multivariate state equation from a univariate state equation.

MultivariateStateEquation(MultivariateStateEquation) - Constructor for class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Copy constructor.

MultivariateTDistribution - Class in dev.nm.stat.distribution.multivariate

The multivariate T distribution or multivariate Student distribution, is a generalization of the one-dimensional (univariate) Student's t-distribution to higher dimensions.

MultivariateTDistribution(int, Vector, Matrix) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution

Constructs an instance with the given mean and scale matrix.

MultivariateTDistribution(int, int) - Constructor for class dev.nm.stat.distribution.multivariate.MultivariateTDistribution

Constructs an instance of the standard t distribution, mean 0, variance 1.

MultivariateTimeSeries<T extends Comparable<? super T>,E extends MultivariateTimeSeries.Entry<T>> - Interface in dev.nm.stat.timeseries.datastructure.multivariate

A multivariate time series is a sequence of vectors indexed by some notion of time.

MultivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.multivariate

This is the TimeSeries.Entry for a multivariate time series.

mutate() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.Chromosome

Construct a Chromosome by mutation.

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Best2Bin.DeBest2BinCell

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory.ConstrainedCell

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory.DeOptimCell

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin.DeRand1BinCell

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.LocalSearchCell

Mutate by a local search in the neighborhood.

mutate() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell

Mutate by random disturbs in a neighborhood.

Mutex - Class in dev.nm.misc.parallel

Provides mutual exclusive execution of a Runnable.

Mutex() - Constructor for class dev.nm.misc.parallel.Mutex

MVOptimizer - Interface in tech.nmfin.portfoliooptimization.lai2010.optimizer

Solves for the optimal weight using Mean-Variance optimization.

MVOptimizerLongOnly - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer

A long-only MV optimizer.

MVOptimizerLongOnly() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly

MVOptimizerMinWeights - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer

Solves for weights with lower bounds.

MVOptimizerMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights

Constructs the solver with a constraint on the minimum weights (w ≥ w₀).

MVOptimizerNoConstraint - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer

Solves for optimal weights by closed-form expressions of w(η) when there is no limit on short selling.

MVOptimizerNoConstraint() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint

MVOptimizerShrankMean - Class in tech.nmfin.portfoliooptimization.lai2010.optimizer

Shrinks the mean towards average before passing the inputs to another MVOptimizer.

MVOptimizerShrankMean(MVOptimizer, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean

MWC8222 - Class in dev.nm.stat.random.rng.univariate.uniform

Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator.

MWC8222() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.MWC8222

Construct a random number generator to sample uniformly from [0, 1].

MyCutter(ILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryMixedCutMinimizer.MyCutter

Construct a Gomory mixed cutter.

MyCutter(PureILPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.GomoryPureCutMinimizer.MyCutter

Construct a Gomory pure cutter.

MySteepestDescent(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent

N

N() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable

Get the number of data points.

n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D

Gets the number of interior y-axis grid points in the solution.

N() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D

Gets the number of interior space-axis grid points in the solution.

n() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the number of interior x-axis grid points in the solution.

n - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

n() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem

Gets the dimension of the square matrices C and As.

n() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem

Gets the dimension of the system, i.e., the dimension of x, the number of variables.

n(int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

Gets the number of columns of A_i.

n() - Method in class dev.nm.stat.cointegration.CointegrationMLE

Get the number of rows of the multivariate time series used in regression.

n - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

sample size

N() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation

N() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation

N() - Method in class dev.nm.stat.descriptive.covariance.Covariance

N() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

N() - Method in class dev.nm.stat.descriptive.moment.Mean

N() - Method in class dev.nm.stat.descriptive.moment.Moments

N() - Method in class dev.nm.stat.descriptive.moment.Skewness

N() - Method in class dev.nm.stat.descriptive.moment.Variance

N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean

N() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

N() - Method in class dev.nm.stat.descriptive.rank.Max

N() - Method in class dev.nm.stat.descriptive.rank.Min

N() - Method in class dev.nm.stat.descriptive.rank.Quantile

N() - Method in interface dev.nm.stat.descriptive.Statistic

Get the size of the sample.

N() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic

N - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity

the number of observations

N - Variable in class tech.nmfin.infantino2010.Infantino2010PCA

N() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

Gets the number of H inversions.

NaiveRule - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting

This pivoting rule chooses the column with the most negative reduced cost.

NaiveRule() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule

name - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable

the variable name

NaN - Static variable in class dev.nm.number.complex.Complex

a number representing the complex Not-a-Number (NaN)

natural() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline

Creates an instance with end conditions which fits natural splines, meaning that the second derivative at both ends are zero.

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

Get the dimension of the Brownian motion (or the number of driving 1D Brownian motions).

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2

Deprecated.

nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionMatrix

Get the number of independent Brownian motions.

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateBrownianSDE

nB() - Method in interface dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateDiscreteSDE

Get the number of independent driving Brownian motions.

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Get the number of independent driving Brownian motions.

nB() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE

Get the number of driving Brownian motions.

nChildren() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

Get the number of children before populating the next generation.

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

nCols() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

nCols() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

nCols() - Method in class dev.nm.misc.datastructure.FlexibleTable

nCols() - Method in interface dev.nm.misc.datastructure.Table

Gets the number of columns.

nColumns() - Method in class dev.nm.misc.datastructure.MathTable

Gets the number of columns in the table.

NEGATIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex

a number representing -∞ + -∞i

NegCetaFunction(Ceta) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.NegCetaFunction

neighbor(V) - Method in interface dev.nm.graph.Edge

Gets the neighboring vertex connected to vertex.

neighbor(V) - Method in class dev.nm.graph.type.SimpleArc

neighbor(V) - Method in class dev.nm.graph.type.SimpleEdge

Get the unique neighboring vertex connected to vertex.

neighbors(V) - Method in interface dev.nm.graph.HyperEdge

Gets the set of neighboring vertices connected to vertex.

neighbors(V) - Method in class dev.nm.graph.type.SimpleArc

neighbors(V) - Method in class dev.nm.graph.type.SimpleEdge

NelderMeadMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2

The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems.

NelderMeadMinimizer(double, double, double, double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer

Construct a Nelder-Mead multivariate minimizer.

NelderMeadMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer

Construct a Nelder-Mead multivariate minimizer.

NelderMeadMinimizer.Solution - Class in dev.nm.solver.multivariate.unconstrained.c2

This is the solution to an optimization problem by the Nelder-Mead method.

nEqualities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

Get the number of equality constraints.

NEUTRON_MASS_M - Static variable in class dev.nm.misc.PhysicalConstants

The neutron mass in kilograms (kg).

NevilleTable - Class in dev.nm.analysis.curvefit.interpolation

Neville's algorithm is a polynomial interpolation algorithm.

NevilleTable(int, OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable

Construct a Neville table of size n, initialized with data {(x, y)}.

NevilleTable(OrderedPairs) - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable

Construct a Neville table of size n, initialized with data {(x, y)}.

NevilleTable() - Constructor for class dev.nm.analysis.curvefit.interpolation.NevilleTable

Construct an empty Neville table.

newActiveList() - Method in interface dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer.ActiveListFactory

Construct a new instance of ActiveList for an Integer Linear Programming problem.

newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.NewCellFactory

Construct a new instance of DEOptimCellFactory for a minimization problem.

newCellFactory() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.NewCellFactoryCtor

Construct a new instance of SimpleCellFactory for a minimization problem.

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution

newDistributions() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution

Get the distributions (possibly differently parameterized) for all states.

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution

newDistributions() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution

newInstance(double[], int, int, DoubleArrayOperation) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

newInstance(Matrix) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.PreconditionerFactory

Construct a new instance of Preconditioner for a coefficient matrix.

newInstance() - Method in interface dev.nm.solver.multivariate.constrained.SubProblemMinimizer.ConstrainedMinimizerFactory

Constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.

newInstance() - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.local.LocalSearchCellFactory.MinimizerFactory

Construct a new instance of Minimizer for a mutation operation.

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution

newMixtureDistribution(Object[]) - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution

Construct a new distribution from a set of parameters, one set per state.

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution

newMixtureDistribution(Object[]) - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BetaMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.ExponentialMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.LogNormalMixtureDistribution

newRandomNumberGenerators() - Method in interface dev.nm.stat.hmm.mixture.distribution.MixtureDistribution

Get the random number generators corresponding to the distributions (possibly differently parameterized) for all states.

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution

newRandomNumberGenerators() - Method in class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution

newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement

newResample() - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject

newResample() - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler

newResample() - Method in interface dev.nm.stat.random.sampler.resampler.multivariate.MultivariateResampler

Gets a resample from the original sample.

newResample() - Method in interface dev.nm.stat.random.sampler.resampler.ObjectResampler

Gets a resample from the original sample.

newResample() - Method in interface dev.nm.stat.random.sampler.resampler.Resampler

Gets a resample from the original sample.

newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2

newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory

newResampler(Matrix) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ModelResamplerFactory

newResampler(Matrix) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.ReturnsResamplerFactory

Constructs a new instance of a re-sampling mechanism.

newSOCPGeneralConstraints(SOCPPortfolioConstraint.Variable...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint

Creates a new SOCPGeneralConstraints so we can add SOCPGeneralConstraint to it.

NewtonCotes - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes

The Newton-Cotes formulae, also called the Newton-Cotes quadrature rules or simply Newton-Cotes rules, are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points.

NewtonCotes(int, NewtonCotes.Type, double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes

Construct an instance of the Newton-Cotes quadrature.

NewtonCotes.Type - Enum in dev.nm.analysis.integration.univariate.riemann.newtoncotes

There are two types of the Newton-Cotes method: OPEN and CLOSED.

NewtonPolynomial - Class in dev.nm.analysis.curvefit.interpolation.univariate

Newton polynomial is the interpolation polynomial for a given set of data points in the Newton form.

NewtonPolynomial() - Constructor for class dev.nm.analysis.curvefit.interpolation.univariate.NewtonPolynomial

NewtonRaphsonImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer.NewtonRaphsonImpl

NewtonRaphsonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent

The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series.

NewtonRaphsonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer

Construct a multivariate minimizer using the Newton-Raphson method.

NewtonRaphsonMinimizer.NewtonRaphsonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent

NewtonRoot - Class in dev.nm.analysis.root.univariate

The Newton-Raphson method is as follows: one starts with an initial guess which is reasonably close to the true root, then the function is approximated by its tangent line (which can be computed using the tools of calculus), and one computes the x-intercept of this tangent line (which is easily done with elementary algebra).

NewtonRoot(double, int) - Constructor for class dev.nm.analysis.root.univariate.NewtonRoot

Constructs an instance of Newton's root finding algorithm.

NewtonSystemRoot - Class in dev.nm.analysis.root.multivariate

This class solves the root for a non-linear system of equations.

NewtonSystemRoot(double, int) - Constructor for class dev.nm.analysis.root.multivariate.NewtonSystemRoot

Constructs an instance of Newton's root finding algorithm for a system of non-linear equations.

newVariation(RealScalarFunction, EqualityConstraints) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory

Construct a new instance of SQPASEVariation for an SQP problem.

newVariation(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.VariationFactory

Construct a new instance of SQPASVariation for an SQP problem.

nExogenousFactors() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the number of factors, excluding the intercept.

next() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator

next(int, UnivariateRealFunction, double, double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.IterativeIntegrator

Compute a refined sum for the integral.

next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes

next(int, UnivariateRealFunction, double, double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson

next() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler

Gets the next random element from the range of the probability distribution.

next(Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim

Gets the next innovation.

next(double) - Method in class dev.nm.stat.dlm.univariate.DLMSim

Get the next innovation.

next() - Method in class dev.nm.stat.hmm.HMMRNG

Gets the next simulated innovation: state and observation.

next() - Method in interface dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator.Generator

Returns the next value in the underlying generated sequence.

next() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator

Returns the next value in the generated sequence.

next() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast

Gets the next forecast.

next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast

Gets the next n-step forecasts.

next() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast

Gets the next forecast.

next(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecast

Gets the next n-step forecasts.

nextDouble() - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG

nextDouble() - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC

nextDouble() - Method in class dev.nm.stat.evt.timeseries.MARMASim

nextDouble() - Method in class dev.nm.stat.hmm.HMMRNG

Gets the next simulated observation.

nextDouble() - Method in class dev.nm.stat.markovchain.SimpleMC

Gets the next simulated state.

nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG

nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG

nextDouble() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

Get the next random double, which is either 1 (success) or 0 (failure).

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969

Deprecated.

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969

nextDouble() - Method in interface dev.nm.stat.random.rng.univariate.RandomNumberGenerator

Get the next random double.

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.ThinRNG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3

nextDouble() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk

nextDouble() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim

nextDouble() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim

nextDouble() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Gets the next simulated observation.

nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0

nextInt() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3

nextLogTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

Performs a Bernoulli trial that succeeds with probability e^p.

nextLogTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

Performs a Bernoulli trial that succeeds with probability e^p.

nextLong() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG

nextLong() - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG

nextLong() - Method in interface dev.nm.stat.random.rng.univariate.RandomLongGenerator

Get the next random long.

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

All built-in linear random number generators in this library ultimately call this function to generate random numbers.

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3

nextLong() - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

nextMatrix() - Method in class dev.nm.stat.test.distribution.pearson.AS159

Constructs a random matrix based on the row and column sums.

nextN(RandomNumberGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils

Generates n random numbers from a given random number generator.

nextN(RandomVectorGenerator, int) - Static method in class dev.nm.stat.random.rng.RNGUtils

Generates n random vectors from a given random vector generator.

nextP(double, double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the evolution of p_t, the conditional probability of being in an uptrend given all information, i.e., \(p_t = P(\alpha_t = 1 | \mathcal{F}_t)\).

nextPair() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim

Get the next random (e2_t, h_t).

nextProposal(Vector, double) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.AnnealingFunction

Gets the next proposal, given the current state and the temperature.

nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.BoxGSAAnnealingFunction

nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.GSAAnnealingFunction

nextProposal(Vector, double) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.HybridMCMC

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.MultipointHybridMCMC

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

Proposes a next state for the system.

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.Metropolis

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.MetropolisHastings

nextProposedState(Vector) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis

nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG

nextRealization() - Method in interface dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationGenerator

Construct a realization of a multivariate stochastic process.

nextRealization() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

nextRealization() - Method in interface dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationGenerator

Construct a realization of a univariate stochastic process.

nextRealization() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

nextState() - Method in class dev.nm.stat.markovchain.SimpleMC

Gets the next simulated state.

nextTime() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

Get the next time point in the time grid.

nextTime() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess

Get the next time point in the time grid.

nextTrial(RandomNumberGenerator, double) - Static method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

Performs a Bernoulli trial that succeeds with probability p.

nextTrial() - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

Performs a Bernoulli trial that succeeds with probability p.

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic

nextVector() - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic

nextVector() - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.IID

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG

nextVector() - Method in interface dev.nm.stat.random.rng.multivariate.RandomVectorGenerator

Get the next random vector.

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG

nextVector() - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox

nextVector() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk

nextVector() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim

nextWeekDay(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Get the next weekday, i.e., skipping Saturdays and Sundays.

nextX(double, double, double) - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the evolution of x_t, logit of the conditional probability from (0, 1) onto \((-\infty, +\infty)\), i.e., \(x_t = \log{\frac{p_t}{1-p_t}}\).

nFactors() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis

Gets the number of factors.

nFactors() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the number of variables in the original data.

nFactors() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the number of factors, including the intercept if any.

nGreaterThanInequalities() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

Get the number of greater-than-or-equal-to constraints.

nGroups() - Method in class dev.nm.stat.test.HypothesisTest

Get the number of groups of observations.

nidx - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of unique values in dis_G_list

nlshrink_tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result

Gets nonlinear shrinkage tau in ascending order.

NMSAAM - Class in tech.nmfin.portfoliooptimization.nmsaam

NMSAAM(double, double, int, int, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM

NMSAAM(double, double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM

NMSAAM(double) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM

NMSAAM(double, double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM

NMSAAM(double, int) - Constructor for class tech.nmfin.portfoliooptimization.nmsaam.NMSAAM

nNoChanges - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

nNonZeros() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseStructure

Get the number of non-zero entries in the structure.

nNonZeros() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

no(double) - Method in interface dev.nm.number.DoubleUtils.ifelse

Return value for a false element of test.

nObs() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis

Gets the number of observations.

nObs() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the number of observations in the original data; sample size.

nObs() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the number of observations.

nObs() - Method in class dev.nm.stat.test.HypothesisTest

Get the total number of observations.

NoChangeOfVariable - Class in dev.nm.analysis.integration.univariate.riemann.substitution

This is a dummy substitution rule that does not change any variable.

NoChangeOfVariable(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable

Construct an NoChangeOfVariable substitution rule.

NoConstraints - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint

Weights are unconstrained and NoConstraints.constraints() returns null.

NoConstraints() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoConstraints

Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.BFS.Node

Constructs a node for a spanning tree.

Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.DFS.Node

Constructs a node for a spanning tree.

Node(V) - Constructor for class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

Constructs a node for a spanning tree.

NoModelFitted() - Constructor for exception tech.nmfin.meanreversion.elliott2005.ElliottOnlineFilter.NoModelFitted

noNaN(double[]) - Static method in class dev.nm.number.DoubleUtils

Remove the NaN from an array.

NonlinearFit - Class in dev.nm.stat.evt.evd.univariate.fitting.acer

Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess.

NonlinearFit() - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.NonlinearFit

NonlinearFit.Result - Class in dev.nm.stat.evt.evd.univariate.fitting.acer

NonlinearShrinkageEstimator - Class in dev.nm.stat.covariance.nlshrink

The nonlinear shrinkage method for given population eigenvalues.

NonlinearShrinkageEstimator(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator

NonlinearShrinkageEstimator(QuEST.Result) - Constructor for class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator

nonlinearShrunkEigenvalues() - Method in class dev.nm.stat.covariance.nlshrink.NonlinearShrinkageEstimator

Gets the nonlinear shrinkage eigenvalues in ascending order.

NonNegativityConstraintOptimProblem - Class in dev.nm.solver.multivariate.constrained.problem

This is a constrained optimization problem for a function which has all non-negative variables.

NonNegativityConstraintOptimProblem(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.problem.NonNegativityConstraintOptimProblem

Construct a constrained optimization problem with only non-negative variables.

NonNegativityConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

These constraints ensures that for all variables are non-negative.

NonNegativityConstraints(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.NonNegativityConstraints

Construct a lower bound constraints for all variables in a function.

NoPairFoundException - Exception in tech.nmfin.meanreversion.cointegration

NoPairFoundException(String) - Constructor for exception tech.nmfin.meanreversion.cointegration.NoPairFoundException

norm() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

norm(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

norm() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

norm(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the norm of a vector.

norm(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the norm of a vector.

norm() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

norm(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

norm() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

norm() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Compute the length or magnitude or Euclidean norm of a vector, namely, \(\|v\|\).

norm(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Gets the \(L^p\)-norm \(\|v\|_p\) of this vector.

norm() - Method in interface dev.nm.algebra.structure.BanachSpace

|⋅| : B → F
norm assigns a strictly positive length or size to all vectors in the vector space, other than the zero vector.

NormalDistribution - Class in dev.nm.stat.distribution.univariate

The Normal distribution has its density a Gaussian function.

NormalDistribution() - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution

Construct an instance of the standard Normal distribution with mean 0 and standard deviation 1.

NormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.NormalDistribution

Construct a Normal distribution with mean mu and standard deviation sigma.

NormalMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution

The HMM states use the Normal distribution to model the observations.

NormalMixtureDistribution(NormalMixtureDistribution.Lambda[], boolean, boolean) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution

Constructs a Normal distribution for each state in the HMM model.

NormalMixtureDistribution(NormalMixtureDistribution.Lambda[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution

Constructs a Normal distribution for each state in the HMM model.

NormalMixtureDistribution.Lambda - Class in dev.nm.stat.hmm.mixture.distribution

the Normal distribution parameters

NormalOfExpFamily1 - Class in dev.nm.stat.distribution.univariate.exponentialfamily

Normal distribution, univariate, unknown mean, known variance.

NormalOfExpFamily1(double) - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily1

NormalOfExpFamily2 - Class in dev.nm.stat.distribution.univariate.exponentialfamily

Normal distribution, univariate, unknown mean, unknown variance.

NormalOfExpFamily2() - Constructor for class dev.nm.stat.distribution.univariate.exponentialfamily.NormalOfExpFamily2

NormalRNG - Class in dev.nm.stat.random.rng.univariate.normal

This is a random number generator that generates random deviates according to the Normal distribution.

NormalRNG(double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.NormalRNG

Construct a random number generator to sample from the Normal distribution.

NormalRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.normal.NormalRNG

Construct a random number generator to sample from the Normal distribution.

NormalRVG - Class in dev.nm.stat.random.rng.multivariate

A multivariate Normal random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution.

NormalRVG(Vector, Matrix, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG

Constructs a multivariate Normal random vector generator.

NormalRVG(Vector, Matrix, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG

Constructs a multivariate Normal random vector generator.

NormalRVG(Vector, Matrix) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG

Constructs a multivariate Normal random vector generator.

NormalRVG(int) - Constructor for class dev.nm.stat.random.rng.multivariate.NormalRVG

Constructs a standard multivariate Normal random vector generator.

NoRootFoundException - Exception in dev.nm.analysis.root.univariate

This is the Exception thrown when it fails to find a root.

NoRootFoundException(double, double) - Constructor for exception dev.nm.analysis.root.univariate.NoRootFoundException

Construct a NoRootFoundException.

NoShortSelling - Class in tech.nmfin.portfoliooptimization.corvalan2005.constraint

Weights cannot be negative.

NoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.constraint.NoShortSelling

Constructs the constraint with the number of assets in the portfolio.

NoSolution(String) - Constructor for exception dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver.NoSolution

Construct an LinearSystemSolver.NoSolution exception.

notAKnot() - Static method in class dev.nm.analysis.curvefit.interpolation.univariate.CubicSpline

Creates an instance with end conditions which fits not-a-knot splines, meaning that continuity of the third derivative at the second and the next-to-last knots are forced.

nParams() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares

Get the number of parameters for the estimation/fitting.

NPEBPortfolioMomentsEstimator - Class in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb

Uses Non-Parametric Empirical Bayes (NPEB) approach to estimate the first and the second moments of the weighted portfolios.

NPEBPortfolioMomentsEstimator(Matrix, ReturnsMoments.Estimator, MVOptimizer, ReturnsResamplerFactory, int) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.NPEBPortfolioMomentsEstimator

nPopulation() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

Get the size of the population pool, that is the number of chromosomes.

nquant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of points we select between in F[i]

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.SubMatrixBlock

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

nRows() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

nRows() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

nRows() - Method in class dev.nm.misc.datastructure.FlexibleTable

nRows() - Method in interface dev.nm.misc.datastructure.Table

Gets the number of rows.

nSamples() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

Get the number of samples in the empirical distribution.

nSim - Variable in class dev.nm.stat.test.distribution.normality.JarqueBera

nStableIterations - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

nStates() - Method in class dev.nm.stat.markovchain.SimpleMC

Gets the number of states.

nSymbols() - Method in class dev.nm.stat.hmm.discrete.DiscreteHMM

Gets the number of observation symbols per state.

nullDeviance() - Method in class dev.nm.stat.regression.linear.logistic.LogisticResiduals

Gets the null deviance.

nullity() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Get the nullity of A.

nullity(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Deprecated.
Not supported yet.

NullMonitor<S> - Class in dev.nm.misc.algorithm.iterative.monitor

This IterationMonitor does nothing when a new iterate is added.

NullMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.NullMonitor

numberOfChildren(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils

Gets the number of children.

numberOfClusters() - Method in class dev.nm.graph.community.GirvanNewman

Gets the number of connected clusters.

numberOfEdges(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils

Gets the number of edges in this graph.

numberOfParents(DiGraph<V, ? extends Arc<V>>, V) - Static method in class dev.nm.graph.GraphUtils

Gets the number of parents.

numberOfVertices(Graph<V, ?>) - Static method in class dev.nm.graph.GraphUtils

Gets the number of vertices in this graph.

NumberUtils - Class in dev.nm.number

These are the utility functions to manipulate Numbers.

NumberUtils.Comparable<T extends Number> - Interface in dev.nm.number

We need a precision parameter to determine whether two numbers are close enough to be treated as equal.

numint - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of intervals

nVariables() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis

Gets the number of variables in the original data set.

O

ObjectResampler<X> - Interface in dev.nm.stat.random.sampler.resampler

This is the interface of a re-sampler method for objects.

objects() - Method in class dev.nm.combinatorics.Ties

Get the set of objects counted.

observation - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation

the simulated observation

observation - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation

the simulated observation

observation() - Method in class dev.nm.stat.hmm.HmmInnovation

Get the observation associated with the hidden state.

ObservationEquation - Class in dev.nm.stat.dlm.univariate

This is the observation equation in a controlled dynamic linear model.

ObservationEquation(UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation

Construct an observation equation.

ObservationEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation

Construct an observation equation.

ObservationEquation(double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation

Construct a time-invariant an observation equation.

ObservationEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation

Construct a time-invariant an observation equation.

ObservationEquation(ObservationEquation) - Constructor for class dev.nm.stat.dlm.univariate.ObservationEquation

Copy constructor.

ODE - Class in dev.nm.analysis.differentialequation.ode.ivp.problem

An ordinary differential equation (ODE) is an equation in which there is only one independent variable and one or more derivatives of a dependent variable with respect to the independent variable, so that all the derivatives occurring in the equation are ordinary derivatives.

ODE(RealScalarFunction, double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE

Construct an ODE of order n together with its initial values.

ODE1stOrder - Class in dev.nm.analysis.differentialequation.ode.ivp.problem

A first order ordinary differential equation (ODE) initial value problem (IVP) takes the following form.

ODE1stOrder(DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Constructs a first order ODE with the given vector-valued function and its initial values.

ODE1stOrder(RealScalarFunction[], double[], double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Constructs a system of first order ODEs {Y_i} with their initial values {y_i0}.

ODE1stOrder(RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Constructs a first order ODE with the given vector-valued function and its initial values.

ODE1stOrder(ODE) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Reduces a high order ODE to a system of first order ODEs.

ODE1stOrderWith2ndDerivative - Class in dev.nm.analysis.differentialequation.ode.ivp.problem

Some ODE solvers require the second derivative for more accurate Taylor series approximation.

ODE1stOrderWith2ndDerivative(RealVectorFunction, RealVectorFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative

Constructs a first order ODE with initial values.

ODE1stOrderWith2ndDerivative(DerivativeFunction, DerivativeFunction, Vector, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrderWith2ndDerivative

Constructs a first order ODE with initial values.

ODEIntegrator - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver

This defines the interface for the numerical integration of a first order ODE, for a sequence of pre-defined steps.

ODESolution - Class in dev.nm.analysis.differentialequation.ode.ivp.solver

Solution to an ODE problem.

ODESolution(double[], Vector[]) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution

Create a solution with estimated values at the given points.

ODESolver - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver

Solver for first order ODE problems.

OLSBeta - Class in dev.nm.stat.regression.linear.ols

Beta coefficient estimator, β^, of an Ordinary Least Square linear regression model.

OLSBeta(Vector, OLSResiduals) - Constructor for class dev.nm.stat.regression.linear.ols.OLSBeta

Constructs an instance of Beta.

OLSRegression - Class in dev.nm.stat.regression.linear.ols

(Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model.

OLSRegression(LMProblem, double) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression

Constructs an OLSRegression instance.

OLSRegression(LMProblem) - Constructor for class dev.nm.stat.regression.linear.ols.OLSRegression

Constructs an OLSRegression instance.

OLSResiduals - Class in dev.nm.stat.regression.linear.ols

This is the residual analysis of the results of an ordinary linear regression model.

OLSResiduals(LMProblem, Vector) - Constructor for class dev.nm.stat.regression.linear.ols.OLSResiduals

Performs the residual analysis for an ordinary linear regression problem.

OLSSolver - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

This class solves an over-determined system of linear equations in the ordinary least square sense.

OLSSolver(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolver

Construct an OLS solver for an over-determined system of linear equations.

OLSSolverByQR - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

This class solves an over-determined system of linear equations in the ordinary least square sense.

OLSSolverByQR(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR

Construct an OLS solver for an over-determined system of linear equations.

OLSSolverBySVD - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

This class solves an over-determined system of linear equations in the ordinary least square sense.

OLSSolverBySVD(double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD

Construct an OLS solver for an over-determined system of linear equations.

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

ONE() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

Get an identity matrix that has the same dimension as this matrix.

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

ONE() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

ONE() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

ONE() - Method in interface dev.nm.algebra.structure.Monoid

The multiplicative element 1 in the group such that for any elements a in the group, the equation 1 × a = a × 1 = a holds.

ONE - Static variable in class dev.nm.analysis.function.polynomial.Polynomial

a polynomial representing 1

ONE() - Method in class dev.nm.analysis.function.polynomial.Polynomial

ONE - Static variable in class dev.nm.number.complex.Complex

a number representing 1.0 + 0.0i

ONE() - Method in class dev.nm.number.complex.Complex

Get one - the number representing 1.0 + 0.0i.

ONE - Static variable in class dev.nm.number.Real

a number representing 1

ONE() - Method in class dev.nm.number.Real

OneDimensionTimeSeries<T extends Comparable<? super T>> - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime

This class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate).

OneDimensionTimeSeries(MultivariateTimeSeries<T, ? extends MultivariateTimeSeries.Entry<T>>, int) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries

Construct a univariate realization from a multivariate realization by taking one of its dimension (coordinate).

ones(int, int, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a matrix of the same scalar, e.g.,1.

ones(int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a matrix of 1's.

oneSidedPvalue(ProbabilityDistribution, double) - Static method in class dev.nm.stat.test.HypothesisTest

The one-sided p-value is the probability of observing a test statistic at least as extreme as the one observed.

OneWayANOVA - Class in dev.nm.stat.test.mean

The One-Way ANOVA test tests for the equality of the means of several groups.

OneWayANOVA(double[]...) - Constructor for class dev.nm.stat.test.mean.OneWayANOVA

Perform the one-way ANOVA test to test for the equality of the means of several groups.

OnlineInterpolator - Interface in dev.nm.analysis.curvefit.interpolation

An online interpolator allows dynamically adding more points for interpolation.

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

opposite() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

Get the opposite of this matrix.

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

opposite() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

opposite() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

opposite() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

opposite(Vector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Multiples a vector by -1, element-by-element.

opposite() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

opposite() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

opposite() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Get the opposite of this vector.

opposite() - Method in interface dev.nm.algebra.structure.AbelianGroup

For each a in G, there exists an element b in G such that a + b = b + a = 0.

opposite() - Method in class dev.nm.analysis.function.polynomial.Polynomial

opposite() - Method in class dev.nm.number.complex.Complex

opposite() - Method in class dev.nm.number.Real

optimalModelByAIC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

Selects the optimal ARIMA model by AIC.

optimalModelByAICC() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.AutoARIMAFit

Selects the optimal ARIMA model by AICC.

optimalWeights(Matrix, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel

Computes the weights based on given historical returns and the risk-aversion index λ.

OptimalWeights(Vector, Ceta.PortfolioMoments) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights

optimalWeights(Vector, Matrix, double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizer

Solves for the optimal weights given the moments, lambda, and eta.

optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerLongOnly

optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights

optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerNoConstraint

optimalWeights(Vector, Matrix, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerShrankMean

Optimizer<P,S> - Interface in dev.nm.solver

Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives.

OptimProblem - Interface in dev.nm.solver.problem

This is an optimization problem that minimizes a real valued objective function, one or multi dimension.

order() - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector

Get the order of this predictor-corrector pair.

order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1

order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2

order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3

order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4

order() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5

order(double[], boolean) - Static method in class dev.nm.number.DoubleUtils

Returns a permutation which rearranges its first argument into ascending or descending order.

order(double[]) - Static method in class dev.nm.number.DoubleUtils

Returns a permutation which rearranges its first argument into ascending or descending order.

order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator

order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

order() - Method in interface dev.nm.stat.random.rng.univariate.uniform.linear.LinearCongruentialGenerator

Get the order of recursion.

order() - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

OrderedPairs - Interface in dev.nm.analysis.function.tuple

Cartesian products and binary relations (and hence the ubiquitous functions) are defined in terms of ordered pairs.

OrderStatisticsDistribution - Class in dev.nm.stat.evt.evd.univariate

The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.

OrderStatisticsDistribution(ProbabilityDistribution, int, int) - Constructor for class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

Create an instance with the probability distribution of \(X\), the number of iid samples to be drawn, and the order statistic.

ORIGIN - Static variable in class dev.nm.misc.datastructure.time.JodaTimeUtils

This is the reference time "origin": 1970 January 1, midnight, UTC.

OrnsteinUhlenbeckProcess - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

This class represents a univariate Ornstein-Uhlenbeck (OU) process.

OrnsteinUhlenbeckProcess(double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

Construct a univariate OU process with unit volatility.

OrnsteinUhlenbeckProcess(double, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

Construct a univariate OU process.

OrnsteinUhlenbeckProcess(OrnsteinUhlenbeckProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

Copy constructor.

OrStopConditions - Class in dev.nm.misc.algorithm.stopcondition

Combines an arbitrary number of stop conditions, terminating when the first condition is met.

OrStopConditions(StopCondition...) - Constructor for class dev.nm.misc.algorithm.stopcondition.OrStopConditions

OrthogonalPolynomialFamily - Interface in dev.nm.analysis.integration.univariate.riemann.gaussian.rule

This factory class produces a family of orthogonal polynomials.

OUFitting - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

This interface defines an estimation procedure to fit a univariate Ornstein-Uhlenbeck process.

OUFittingMLE - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

This class fits a univariate Ornstein-Uhlenbeck process by using MLE.

OUFittingMLE(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE

Create an instance with the option whether to estimate the volatility parameter.

OUFittingMLE() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingMLE

Create an instance that estimates the volatility parameter σ.

OUFittingOLS - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

This class fits a univariate Ornstein-Uhlenbeck process by using least squares regression.

OUFittingOLS(boolean) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS

Create an instance with the option whether to estimate the volatility parameter.

OUFittingOLS() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUFittingOLS

Create an instance that estimates the volatility parameter σ.

OUProcess - Interface in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

OUSim - Class in dev.nm.stat.stochasticprocess.univariate.sde.process.ou

This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process.

OUSim(OUProcess, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

Create an OU process simulator.

OUSim(OUProcess, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

Create an OU process simulator.

OUSim(OUProcess, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

Create an OU process simulator using the overall mean as the starting value.

OUSim(OUProcess) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

Create an OU process simulator with a time interval of 1, using the overall mean as the starting value.

outcome() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass

Gets the outcome.

OuterProduct - Class in dev.nm.algebra.linear.matrix.doubles.operation

The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.

OuterProduct(Vector, Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.OuterProduct

outgoingArcs(V) - Method in interface dev.nm.graph.DiGraph

Gets the set of all outgoing arcs associated with a vertex in this graph.

outgoingArcs(V) - Method in class dev.nm.graph.type.SparseDiGraph

outgoingArcs(V) - Method in class dev.nm.graph.type.SparseTree

outgoingArcs(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial

overdispersion(Vector, Vector, int) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution

Over-dispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on the nominal variance of a given simple statistical model.

overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma

overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian

overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian

overdispersion(Vector, Vector, int) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson

overdispersion() - Method in class dev.nm.stat.regression.linear.glm.GLMResiduals

Gets the over-dispersion.

overdispersion() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMResiduals

Computes the over-dispersion.

P

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination

Get the permutation matrix, P, such that P * A = L * U.

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

Gets P, the pivoting matrix in the QR decomposition.

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get P, the pivoting matrix in the QR decomposition.

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle

P() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU

P() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition

Get the permutation matrix P as in P * A = L * U.

p() - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the number of interior y-axis grid points in the solution.

p() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction

p() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized

Get p, the shape parameter.

p() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem

Gets the dimension of the system, i.e., p = the dimension of y, the number of variables.

p() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem

Gets the size of b.

p - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

p - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of variables

p() - Method in class dev.nm.stat.evt.timeseries.MARMAModel

Get the number of AR terms.

p - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda

the success probability in each trial

p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState

Gets the momentum.

p() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Gets the current momentum.

p() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the number of AR terms.

p() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Get the order of the VECM model.

p() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the number of AR terms.

p() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

Get the number of GARCH terms.

p - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Package - Class in dev.nm.misc.license

Pair - Class in dev.nm.analysis.function.tuple

An ordered pair (x,y) is a pair of mathematical objects.

Pair(double, double) - Constructor for class dev.nm.analysis.function.tuple.Pair

Construct a pair.

PairComparatorByAbscissaFirst - Class in dev.nm.analysis.function.tuple

PairComparatorByAbscissaFirst() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst

PairComparatorByAbscissaFirst(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaFirst

PairComparatorByAbscissaOnly - Class in dev.nm.analysis.function.tuple

PairComparatorByAbscissaOnly() - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly

PairComparatorByAbscissaOnly(double) - Constructor for class dev.nm.analysis.function.tuple.PairComparatorByAbscissaOnly

PairingCheck - Interface in tech.nmfin.meanreversion.cointegration.check

PairingModel - Interface in tech.nmfin.meanreversion.cointegration

Given a set of symbols, their prices and other information, we find mean reverting pairs for trading.

PairingModel1 - Class in tech.nmfin.meanreversion.cointegration

PairingModel1(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel1

PairingModel2 - Class in tech.nmfin.meanreversion.cointegration

PairingModel2(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel2

PairingModel3 - Class in tech.nmfin.meanreversion.cointegration

PairingModel3(double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel3

PairingModel4 - Class in tech.nmfin.meanreversion.cointegration

PairingModel4(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel4

PairingModel5 - Class in tech.nmfin.meanreversion.cointegration

PairingModel5(double, double, double, double) - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModel5

PairingModelUtils - Class in tech.nmfin.meanreversion.cointegration

PairingModelUtils() - Constructor for class tech.nmfin.meanreversion.cointegration.PairingModelUtils

PanelData<S> - Class in dev.nm.stat.regression.linear.panel

A panel data refers to multi-dimensional data frequently involving measurements over time.

PanelData(String, String, String[]) - Constructor for class dev.nm.stat.regression.linear.panel.PanelData

Constructs a panel of two-dimensional data.

PanelData.Row - Class in dev.nm.stat.regression.linear.panel

This is one row of the data in a panel.

PanelData.Transformation - Interface in dev.nm.stat.regression.linear.panel

Transforms the data, e.g., taking log.

PanelRegression - Interface in dev.nm.stat.regression.linear.panel

Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two-dimensional (cross sectional/times series) panel data.

parallel - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

This indicate if the algorithm is to run in parallel (multi-core).

ParallelDoubleArrayOperation - Class in dev.nm.number.doublearray

This is a multi-threaded implementation of the array math operations.

ParallelDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.ParallelDoubleArrayOperation

ParallelExecutor - Class in dev.nm.misc.parallel

This class provides a framework for executing an algorithm in parallel.

ParallelExecutor() - Constructor for class dev.nm.misc.parallel.ParallelExecutor

Creates an instance using default concurrency number, which is the number of available processors returned by

ParallelExecutor(String) - Constructor for class dev.nm.misc.parallel.ParallelExecutor

Creates an instance with a specified executor name.

ParallelExecutor(int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor

Creates an instance with a specified concurrency number.

ParallelExecutor(String, int) - Constructor for class dev.nm.misc.parallel.ParallelExecutor

Creates an instance with a specified concurrency number, and a name of the executor.

parent() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

Gets the parent of the node.

parent(V) - Method in interface dev.nm.graph.Tree

Gets the unique parent of a vertex.

parent(V) - Method in class dev.nm.graph.type.SparseTree

parent(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

parents(V) - Method in interface dev.nm.graph.DiGraph

Gets the set of all parents of this vertex.

parents(V) - Method in class dev.nm.graph.type.SparseDiGraph

parents(V) - Method in class dev.nm.graph.type.SparseTree

parents(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

parse(String) - Static method in class dev.nm.number.NumberUtils

Construct a number from a String.

parseArray(String...) - Static method in class dev.nm.number.NumberUtils

Convert an array of numbers in String to an array of numbers in Number.

PartialDerivativesByCenteredDifferencing - Class in dev.nm.analysis.curvefit.interpolation.bivariate

This implementation computes the partial derivatives by centered differencing.

PartialDerivativesByCenteredDifferencing() - Constructor for class dev.nm.analysis.curvefit.interpolation.bivariate.PartialDerivativesByCenteredDifferencing

PartialFunction - Class in dev.nm.analysis.function.tuple

A partial function from X to Y is a function f: X' → Y, where X' is a subset of X.

PartialFunction(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.PartialFunction

Construct a partial function from {(x,y)}.

paste(Collection<String>, String) - Static method in class dev.nm.misc.StringUtils

Concatenates Strings into one String.

path - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

PattonPolitisWhite2009 - Class in dev.nm.stat.random.sampler.resampler.bootstrap.block

This class implements the stationary and circular block bootstrapping method with optimized block length.

PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009(double[], long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009(double[], PattonPolitisWhite2009ForObject.Type) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009(double[]) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject<X> - Class in dev.nm.stat.random.sampler.resampler.bootstrap.block

This class implements the stationary and circular block bootstrapping method with optimized block length.

PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject(X[], Class<X>, PattonPolitisWhite2009ForObject.AutoCorrelationForObject, PattonPolitisWhite2009ForObject.AutoCovarianceForObject, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, RandomLongGenerator, RandomNumberGenerator) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject(X[], Class<X>, long, PattonPolitisWhite2009ForObject.Type, ConcurrentCachedRLG, ConcurrentCachedRNG) - Constructor for class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

Constructs a block bootstrap sample generator.

PattonPolitisWhite2009ForObject.AutoCorrelationForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block

PattonPolitisWhite2009ForObject.AutoCovarianceForObject - Interface in dev.nm.stat.random.sampler.resampler.bootstrap.block

PattonPolitisWhite2009ForObject.Type - Enum in dev.nm.stat.random.sampler.resampler.bootstrap.block

PCA - Interface in dev.nm.stat.factor.pca

Principal Component Analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.

PCAbyEigen - Class in dev.nm.stat.factor.pca

This class performs Principal Component Analysis (PCA) on a data matrix, using eigen decomposition on the correlation or covariance matrix.

PCAbyEigen(Matrix, boolean, Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen

Performs Principal Component Analysis, using the eigen method, on a data matrix with an optional correlation (or covariance) matrix provided.

PCAbyEigen(Matrix, boolean) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen

Performs Principal Component Analysis, using the eigen method, on a data matrix.

PCAbyEigen(Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbyEigen

Performs Principal Component Analysis, using the eigen method and using correlation matrix, on a data matrix.

PCAbySVD - Class in dev.nm.stat.factor.pca

This class performs Principal Component Analysis (PCA) on a data matrix, using the preferred Singular Value Decomposition (SVD) method.

PCAbySVD(Matrix, Vector, Vector) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD

Performs Principal Component Analysis, using the preferred SVD method, on a data matrix with (optional) mean vector and scaling vector provided.

PCAbySVD(Matrix, boolean, boolean) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD

Performs Principal Component Analysis, using the preferred SVD method, on a data matrix (possibly centered and/or scaled).

PCAbySVD(Matrix) - Constructor for class dev.nm.stat.factor.pca.PCAbySVD

Performs Principal Component Analysis, using the preferred SVD method, on a centered and scaled data matrix.

PDE - Interface in dev.nm.analysis.differentialequation.pde

A partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives.

PDESolutionGrid2D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference

A solution to a bivariate PDE, which is applicable to methods which produce the solution as a two-dimensional grid.

PDESolutionTimeSpaceGrid1D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference

A solution to an one-dimensional PDE, which is applicable to methods which produce the solution as a grid of time and space.

PDESolutionTimeSpaceGrid2D - Interface in dev.nm.analysis.differentialequation.pde.finitedifference

A solution to a two-dimensional PDE, which is applicable to methods which produce the solution as a three-dimensional grid of time and space.

PDESolver - Interface in dev.nm.analysis.differentialequation.pde

A PDE solver solves a set of PDEs.

PDETimeSpaceGrid1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference

This grid numerically solves a 1D PDE, e.g., using the Crank-Nicolson scheme.

PDETimeSpaceGrid1D(int) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D

Constructs a time-space grid.

PeaksOverThreshold - Class in dev.nm.stat.evt.evd.univariate.fitting.pot

Peaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution (GPD) using maximum likelihood on the observations that are over a given threshold.

PeaksOverThreshold(double) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThreshold

Create an instance for POT method with a given threshold.

PeaksOverThresholdOnClusters - Class in dev.nm.stat.evt.evd.univariate.fitting.pot

Similar to POT, but only use the peak observations in clusters for the parametric estimation.

PeaksOverThresholdOnClusters(ClusterAnalyzer) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.pot.PeaksOverThresholdOnClusters

Create an instance with a ClusterAnalyzer which is used to find clusters from observations.

PearsonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

This is the Pearson method.

PearsonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.PearsonMinimizer

Construct a multivariate minimizer using the Pearson method.

pearsonStat(Matrix, Matrix, boolean) - Static method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest

Compute the Pearson's cumulative test statistic, which asymptotically approaches a χ2 distribution.

penalizedCardinality(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem

Gets the value of a cardinality-penalized function.

penalizedL1(Matrix) - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem

Gets the value of an L1-penalized function.

PenaltyFunction - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod

A function P: Rⁿ -> R is a penalty function for a constrained optimization problem if it has these properties.

PenaltyFunction() - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyFunction

PenaltyMethodMinimizer - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod

The penalty method is an algorithm for solving a constrained minimization problem with general constraints.

PenaltyMethodMinimizer(PenaltyMethodMinimizer.PenaltyFunctionFactory, double, IterativeC2Minimizer) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer

Construct a constrained minimizer using the penalty method.

PenaltyMethodMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer

Construct a constrained minimizer using the penalty method.

PenaltyMethodMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer

Construct a constrained minimizer using the penalty method.

PenaltyMethodMinimizer.PenaltyFunctionFactory - Interface in dev.nm.solver.multivariate.constrained.general.penaltymethod

For each constrained optimization problem, the solver creates a new penalty function for it.

permutation(int, int) - Static method in class dev.nm.analysis.function.FunctionOps

Compute the permutation function.

permutation(int, int) - Static method in class dev.nm.number.big.BigIntegerUtils

Compute the permutation function.

PermutationMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype

A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.

PermutationMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Construct an identity permutation matrix.

PermutationMatrix(int[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Construct a permutation matrix from an 1D double[].

PermutationMatrix(PermutationMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Copy constructor.

perpendicularDistance(Point) - Method in class dev.nm.geometry.LineSegment

PerturbationAroundPoint - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration

The initial population is generated by adding a variance around a given initial.

PerturbationAroundPoint(RealScalarFunction, SimpleCellFactory, int, Vector, Vector, long) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.PerturbationAroundPoint

Generate an initial pool of chromosomes by adding a variance around a given initial.

phi - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

phi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get all the AR coefficients.

phi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get all the AR coefficients.

phiPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the polynomial (1 - φ).

PhysicalConstants - Class in dev.nm.misc

A collection of fundamental physical constants.

PI - Static variable in class dev.nm.number.big.BigDecimalUtils

the value of PI

PI() - Method in class dev.nm.stat.markovchain.SimpleMC

Gets the initial state probabilities.

pi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Get the impact matrix.

PI() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility

Get the coefficients of the linear representation of the time series.

PI_SQ - Static variable in class dev.nm.misc.Constants

\(\pi^2\)

Pivot(int, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot

Construct a pivot.

PLANCK_H - Static variable in class dev.nm.misc.PhysicalConstants

The Planck constant \(h\) in joule seconds (J s).

PLANCK_REDUCED_HBAR - Static variable in class dev.nm.misc.PhysicalConstants

The reduced Planck constant (or Dirac constant) \(\hbar\), defined as the Planck constant divided by 2π, in joule seconds (J s).

plusWeekdayPeriod(DateTime, Period) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Add a weekday-period (i.e., skipping weekends) to a DateTime.

Point - Class in dev.nm.geometry

Represent a n-dimensional point.

Point(double...) - Constructor for class dev.nm.geometry.Point

Create a point with given coordinates.

Point(Vector) - Constructor for class dev.nm.geometry.Point

Create a point with given coordinates.

points(double, double) - Method in interface dev.nm.solver.univariate.GridSearchMinimizer.GridDefinition

PoissonDistribution - Class in dev.nm.stat.distribution.univariate

The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.

PoissonDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.PoissonDistribution

Construct a Poisson distribution.

PoissonEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2

Poisson's equation is an elliptic PDE that takes the following general form.

PoissonEquation2D(double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.PoissonEquation2D

Constructs a Poisson's equation problem.

PoissonMixtureDistribution - Class in dev.nm.stat.hmm.mixture.distribution

The HMM states use the Poisson distribution to model the observations.

PoissonMixtureDistribution(Double[]) - Constructor for class dev.nm.stat.hmm.mixture.distribution.PoissonMixtureDistribution

Constructs a Poisson distribution for each state in the HMM model.

PolygonalChain - Interface in dev.nm.geometry.polyline

A polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected series of line segments.

PolygonalChainByArray - Class in dev.nm.geometry.polyline

An immutable PolygonalChain that is backed by an ArrayList.

PolygonalChainByArray(List<? extends Point>) - Constructor for class dev.nm.geometry.polyline.PolygonalChainByArray

Create a new instance which uses the given vertices.

Polynomial - Class in dev.nm.analysis.function.polynomial

A polynomial is a UnivariateRealFunction that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents.

Polynomial(double...) - Constructor for class dev.nm.analysis.function.polynomial.Polynomial

Construct a polynomial from an array of coefficients.

Polynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.Polynomial

Copy constructor.

PolyRoot - Class in dev.nm.analysis.function.polynomial.root

This is a solver for finding the roots of a polynomial equation.

PolyRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.PolyRoot

PolyRootSolver - Interface in dev.nm.analysis.function.polynomial.root

A root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes.

pop() - Method in interface dev.nm.misc.algorithm.bb.ActiveList

Get the next node.

population - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

This is the (current) population pool.

PortfolioMoments(double, double) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.ceta.Ceta.PortfolioMoments

PortfolioOptimizationAlgorithm - Interface in tech.nmfin.portfoliooptimization

Computes the optimal weights based only on returns.

PortfolioOptimizationAlgorithm.CovarianceEstimator - Interface in tech.nmfin.portfoliooptimization

Define how the expected covariances of an asset for a future period is computed.

PortfolioOptimizationAlgorithm.MeanEstimator - Interface in tech.nmfin.portfoliooptimization

Define how the expected mean of an asset for a future period is computed.

PortfolioOptimizationAlgorithm.SampleCovarianceEstimator - Class in tech.nmfin.portfoliooptimization

Estimate the expected covariances of an asset using sample covariances.

PortfolioOptimizationAlgorithm.SampleMeanEstimator - Class in tech.nmfin.portfoliooptimization

Estimate the expected mean of an asset using sample mean.

PortfolioOptimizationAlgorithm.SymbolLookup - Interface in tech.nmfin.portfoliooptimization

Provides a lookup for product symbols and indices.

PortfolioRiskExactSigma - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Constructs the constraint coefficient arrays of the portfolio risk term in the compact form.

PortfolioRiskExactSigma(Matrix, PortfolioRiskExactSigma.MatrixRoot) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma

Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.

PortfolioRiskExactSigma(Matrix, Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma

Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.

PortfolioRiskExactSigma(Matrix) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma

Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.

PortfolioRiskExactSigma.DefaultRoot - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Computes the matrix root by Cholesky and on failure by MatrixRootByDiagonalization.

PortfolioRiskExactSigma.Diagonalization - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Computes the matrix root by MatrixRootByDiagonalization.

PortfolioRiskExactSigma.MatrixRoot - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Specifies the method to compute the root of a matrix.

PortfolioUtils - Class in tech.nmfin.portfoliooptimization

position(int, int, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

position() - Method in interface tech.nmfin.meanreversion.volarb.MRModel

Gets the current target position computed by this model.

position() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged

POSITIVE_INFINITY - Static variable in class dev.nm.number.complex.Complex

a number representing +∞ + ∞i

PositiveDefiniteMatrixByPositiveDiagonal - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite

This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.

PositiveDefiniteMatrixByPositiveDiagonal(Matrix, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveDefiniteMatrixByPositiveDiagonal

Constructs a positive definite matrix by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.

PositiveSemiDefiniteMatrixNonNegativeDiagonal - Class in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite

This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by forcing the negative diagonal entries in the eigen decomposition to 0.

PositiveSemiDefiniteMatrixNonNegativeDiagonal(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite.PositiveSemiDefiniteMatrixNonNegativeDiagonal

Constructs a positive semi-definite matrix by forcing the negative diagonal entries in the eigen decomposition to 0.

pow(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Pow - Class in dev.nm.algebra.linear.matrix.doubles.operation

This is a square matrix A to the power of an integer n, Aⁿ.

Pow(Matrix, int, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow

Construct the power matrix Aⁿ so that Aⁿ = base^scale * B

Pow(Matrix, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.Pow

Construct the power matrix Aⁿ so that Aⁿ = (1e100)^scale * B

pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

pow(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Takes a power of a vector, element-by-element.

pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

pow(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

pow(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Take the exponentiation of all entries in this vector, entry-by-entry.

pow(int) - Method in class dev.nm.analysis.function.polynomial.Polynomial

pow(BigDecimal, BigDecimal) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute a to the power of b.

pow(BigDecimal, BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute a to the power of b.

pow(BigDecimal, int) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute a to the power of n, where n is an integer.

pow(BigDecimal, int, int) - Static method in class dev.nm.number.big.BigDecimalUtils

Compute a to the power of n, where n is an integer.

pow(Complex, Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

z₁ to the power z₂.

pow(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Raise each element in an array to the power of the given exponent.

PowellImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer.PowellImpl

PowellMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection

Powell's algorithm, starting from an initial point, performs a series of line searches in one iteration.

PowellMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer

Construct a multivariate minimizer using the Powell method.

PowellMinimizer.PowellImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection

an implementation of Powell's algorithm

PowerLawSingularity - Class in dev.nm.analysis.integration.univariate.riemann.substitution

This transformation is good for an integral which diverges at one of the end points.

PowerLawSingularity(PowerLawSingularity.PowerLawSingularityType, double, double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity

Construct a PowerLawSingularity substitution rule.

PowerLawSingularity.PowerLawSingularityType - Enum in dev.nm.analysis.integration.univariate.riemann.substitution

the type of end point divergence

PrecisionUtils - Class in dev.nm.misc

Precision-related utility functions.

Preconditioner - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

Preconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergence when the system is solved by an iterative method.

PreconditionerFactory - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

This constructs a new instance of Preconditioner for a coefficient matrix.

predict(DerivativeFunction, double, double[], Vector[]) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector

predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector1

predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector2

predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector3

predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector4

predict(DerivativeFunction, double, double[], Vector[]) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.ABMPredictorCorrector5

previousWeekDay(DateTime) - Static method in class dev.nm.misc.datastructure.time.JodaTimeUtils

Get the previous weekday, i.e., skipping Saturdays and Sundays.

price1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

price2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule

This is pivot column selection (pricing) rule.

pricing(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting

This is pivot column selection (pricing) rule.

pricing(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule

This is pivot column selection (pricing) rule.

PrimalDualInteriorPointMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint

Solves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.

PrimalDualInteriorPointMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer

Constructs a Primal Dual Interior Point minimizer to solve Dual Second Order Conic Programming problems.

PrimalDualInteriorPointMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint

This is the solution to a Dual Second Order Conic Programming problem using the Primal Dual Interior Point algorithm.

PrimalDualPathFollowingMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing

The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems.

PrimalDualPathFollowingMinimizer(double, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer

Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.

PrimalDualPathFollowingMinimizer(double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer

Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.

PrimalDualPathFollowingMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer

Constructs a Primal-Dual Path-Following minimizer to solve semi-definite programming problems.

PrimalDualPathFollowingMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing

This is the solution to a Semi-Definite Programming problem using the Primal-Dual Path-Following algorithm.

PrimalDualSolution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint

The vector set {x, s, y} is a solution to both the primal and dual SOCP problems.

PrimalDualSolution(Vector, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution

Construct a solution to a primal and a dual SOCP problems.

prob - Variable in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix

the probability of observing this matrix

probability() - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassFunction.Mass

Gets the probability of the outcome.

ProbabilityDistribution - Interface in dev.nm.stat.distribution.univariate

A univariate probability distribution completely characterizes a random variable by stipulating the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous).

ProbabilityMassFunction<X> - Interface in dev.nm.stat.distribution.discrete

A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value.

ProbabilityMassFunction.Mass<X> - Class in dev.nm.stat.distribution.discrete

Stores a possible outcome for a probability distribution and its associated probability.

ProbabilityMassQuantile<X> - Class in dev.nm.stat.distribution.discrete

As probability mass function is discrete, there are gaps between values in the domain of its cdf, The quantile function is: \[ Q(p)\,=\,\inf\left\{ x\in R : p \le F(x) \right\} \]

ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>, double) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile

Constructs the quantile function for a probability mass function.

ProbabilityMassQuantile(Iterable<ProbabilityMassFunction.Mass<X>>) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile

Constructs the quantile function for a probability mass function.

ProbabilityMassSampler<X> - Class in dev.nm.stat.distribution.discrete

A random sampler that is constructed ad-hoc from a list of values and their probabilities.

ProbabilityMassSampler(List<ProbabilityMassFunction.Mass<X>>, RandomLongGenerator) - Constructor for class dev.nm.stat.distribution.discrete.ProbabilityMassSampler

Creates an instance with the probable values and an RNG.

problem - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

Problem(Matrix, Vector, Vector) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.Problem

problem - Variable in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

problem() - Method in class dev.nm.stat.covariance.covarianceselection.lasso.CovarianceSelectionLASSO

Get the original covariance selection problem.

problem() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection

Returns the original GLM problem.

process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightPhase1

Find a feasible table, if any.

process() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.FerrisMangasarianWrightScheme2

Remove equalities and free variables, if possible.

product(GivensMatrix[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Given an array of Givens matrices {G_i}, computes G, where G = G₁ * G₂ * ...

product(List<GivensMatrix>) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

product(HouseholderReflection[], int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Compute Q from Householder matrices {Q_i}.

product(HouseholderReflection[], int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Compute Q from Householder matrices {Q_i}.

ProductOfWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification

Defines portfolio diversification as \[ D(w) = \prod_i w_i \]

ProductOfWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights

Projection - Class in dev.nm.algebra.linear.vector.doubles.operation

Project a vector v on another vector w or a set of vectors (basis) {w_i}.

Projection(Vector, List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection

Project a vector v onto a set of basis {w_i}.

Projection(Vector, Vector[]) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection

Project a vector v onto a set of basis {w_i}.

Projection(Vector, Vector) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.Projection

Project a vector v onto another vector.

propagate() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D

Propagates the grid to the next time step by solving \(Au=d\).

proportionVar() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the proportion of overall variance explained by each of the principal components.

proportionVar(int) - Method in interface dev.nm.stat.factor.pca.PCA

Gets the proportion of overall variance explained by the i-th principal component.

proportionVar() - Method in class dev.nm.stat.factor.pca.PCAbyEigen

ProposalFunction - Class in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction

A proposal function goes from the current state to the next state, where a state is a vector.

ProposalFunction(int, int) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction.ProposalFunction

PROTON_ELECTRON_MASS_RATIO - Static variable in class dev.nm.misc.PhysicalConstants

The proton-electron mass ratio \(m_p/m_e\) (dimensionless).

PROTON_MASS_MP - Static variable in class dev.nm.misc.PhysicalConstants

The proton mass \(m_p\) in kilograms (kg).

PrZt0() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Computes the stationary probability of in state Z_t = 0.

PrZt1() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

Computes the stationary probability of in state Z_t = 1.

PseudoInverse - Class in dev.nm.algebra.linear.matrix.doubles.operation

The Moore-Penrose pseudo-inverse of an m x n matrix A is A⁺.

PseudoInverse(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse

Construct the Moore-Penrose pseudo-inverse matrix of a matrix.

PseudoInverse(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.PseudoInverse

Construct the Moore-Penrose pseudo-inverse matrix of A.

psi() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Gets the estimated (optimal) psi, E(ee'), p.

psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the coefficients of the deterministic terms.

psi() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Get the coefficients of the deterministic terms.

psi() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the coefficients of the deterministic terms.

psi(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCovariance

PureILPProblem - Class in dev.nm.solver.multivariate.constrained.integer.linear.problem

This is a pure integer linear programming problem, in which all variables are integral.

PureILPProblem(Vector, LinearGreaterThanConstraints, LinearLessThanConstraints, LinearEqualityConstraints, BoxConstraints, double) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.problem.PureILPProblem

Construct a pure ILP problem.

pValue() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Calculates the p-value of the test statistics, given the degree of freedom.

pValue() - Method in class dev.nm.stat.test.distribution.AndersonDarling

pValue() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples

pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample

pValue() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples

pValue() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

pValue() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera

pValue() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors

pValue() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk

pValue() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest

pValue() - Method in class dev.nm.stat.test.HypothesisTest

Get the p-value for the test statistics.

pValue() - Method in class dev.nm.stat.test.mean.OneWayANOVA

pValue() - Method in class dev.nm.stat.test.mean.T

pValue() - Method in class dev.nm.stat.test.rank.KruskalWallis

pValue() - Method in class dev.nm.stat.test.rank.SiegelTukey

pValue() - Method in class dev.nm.stat.test.rank.VanDerWaerden

pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Compute the two-sided p-value for a critical value.

pValue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

pValue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Compute the two-sided p-value for a critical value.

pValue() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity

pValue() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller

pValue() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce

Deprecated.

pValue() - Method in class dev.nm.stat.test.variance.Bartlett

pValue() - Method in class dev.nm.stat.test.variance.BrownForsythe

pValue() - Method in class dev.nm.stat.test.variance.F

pValue() - Method in class dev.nm.stat.test.variance.Levene

pValueAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling

Gets the alternative p-value (adjusted for ties).

pvalueZ1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

Get the p-value for Z1.

pvalueZ2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

Get the p-value for Z2.

pw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of each distinct eigenvalues

pzw - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

number of eigenvalues that are less or equals to zero

Q

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition

Returns the rotation matrix.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization

Gets Q, such that Q * A * Q = T.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition

Get Q as in Q * D * Q' = A.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.HessenbergDecomposition

Gets the Q matrix, where \[ Q = (Q_1 \times ...

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

Gets the Q matrix as in the real Schur canonical form Q'AQ = T.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm

Gets the Q matrix as in Q'AQ = D, where D is diagonal and Q is orthogonal.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

Gets the Q matrix in the QR decomposition.

Q() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

Q() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get the orthogonal Q matrix in the QR decomposition, A = QR.

q() - Method in class dev.nm.analysis.function.special.beta.BetaRegularized

Get q, the shape parameter.

q() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

Gets the number of A matrices.

q() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter

q() - Method in class dev.nm.stat.evt.timeseries.MARMAModel

Get the number of MA terms.

q() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the number of MA terms.

q() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the number of MA terms.

q() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

Get the number of ARCH terms.

q - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

QPbySOCPMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp

We first convert a QP problem to an equivalent SOCP problem and then solve it using an SOCP solver.

QPbySOCPMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer

Constructs an SOCP minimizer to solve quadratic programming problems.

QPbySOCPMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp

QPConstraint - Interface in tech.nmfin.portfoliooptimization.markowitz.constraints

This interface allows adding constraints to a Quadratic Programming problem solving w_eff, the efficient frontier or the optimal allocation of assets.

QPDualActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset

This implementation solves a Quadratic Programming problem using the dual active set algorithm.

QPDualActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer

QPDualActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer

QPDualActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset

This is the solution to a Quadratic Programming problem using the Dual Active Set algorithm.

QPException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp

This is the exception thrown when there is an error solving a quadratic programming problem.

QPException() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPException

Construct an instance of QPException.

QPInfeasible - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp

This is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i.e., no solution.

QPInfeasible() - Constructor for exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPInfeasible

QPMinimizer - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver

A typedef for QP minimizer.

QPMinWeights - Class in tech.nmfin.portfoliooptimization.markowitz.constraints

QPMinWeights(Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

QPMinWeights(double...) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

QPMinWeights(int, double) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPMinWeights

QPNoConstraint - Class in tech.nmfin.portfoliooptimization.markowitz.constraints

Deprecated.
This constraint means that you can borrow indefinitely, which makes very little economics meaning.

QPNoConstraint() - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoConstraint

Deprecated.

QPNoShortSelling - Class in tech.nmfin.portfoliooptimization.markowitz.constraints

QPNoShortSelling(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPNoShortSelling

QPPrimalActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset

This implementation solves a Quadratic Programming problem using the Primal Active Set algorithm.

QPPrimalActiveSetMinimizer(double, int, boolean) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer

Constructs a Primal Active Set minimizer to solve quadratic programming problems.

QPPrimalActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer

Constructs a Primal Active Set minimizer to solve quadratic programming problems.

QPPrimalActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset

This is the solution to a Quadratic Programming problem using the Primal Active Set algorithm.

QPProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem

Quadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables.

QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem.

QPProblem(QuadraticFunction, LinearGreaterThanConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem with linear inequality constraints.

QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem with linear equality and greater-than-or-equal-to constraints.

QPProblem(QuadraticFunction, LinearGreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem with linear greater-than-or-equal-to constraints.

QPProblem(QuadraticFunction, LinearEqualityConstraints, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem with linear equality and less-than-or-equal-to constraints.

QPProblem(QuadraticFunction, LinearLessThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Construct a quadratic programming problem with linear less-than-or-equal-to constraints.

QPProblem(QPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblem

Copy constructor.

QPProblemOnlyEqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem

A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function.

QPProblemOnlyEqualityConstraints(QuadraticFunction, LinearEqualityConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem.QPProblemOnlyEqualityConstraints

Construct a quadratic programming problem with only equality constraints.

QPSimpleMinimizer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp

These are the utility functions to solve simple quadratic programming problems that admit analytical solutions.

QPSimpleMinimizer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer

QPSolution - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp

This is a solution to a quadratic programming problem.

QPtoSOCPTransformer - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp

Transforms a QPProblem to a SOCPGeneralProblem.

QPtoSOCPTransformer() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer

QPUnity - Class in tech.nmfin.portfoliooptimization.markowitz.constraints

QPUnity(int) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPUnity

QPWeightsLimit - Class in tech.nmfin.portfoliooptimization.markowitz.constraints

QPWeightsLimit(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.markowitz.constraints.QPWeightsLimit

QR - Class in dev.nm.algebra.linear.matrix.doubles.factorization.qr

QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.

QR(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

Run the QR decomposition on a matrix.

QR(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

Run the QR decomposition on a matrix.

QRAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

The QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a matrix.

QRAlgorithm(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

Runs the QR algorithm on a square matrix.

QRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

Runs the QR algorithm on a square matrix.

QRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

Runs the QR algorithm on a square matrix.

QRDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.qr

QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.

Qs() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

Gets the list of Q_i's produced in the process of the QR algorithm (if keepQs is set to true).

Qt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.EigenDecomposition

Get Q' as in Q * D * Q' = A.

QuadraticFunction - Class in dev.nm.analysis.function.rn2r1

A quadratic function takes this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).

QuadraticFunction(Matrix, Vector, double) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction

Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).

QuadraticFunction(Matrix, Vector) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction

Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p\).

QuadraticFunction(QuadraticFunction) - Constructor for class dev.nm.analysis.function.rn2r1.QuadraticFunction

Copy constructor.

QuadraticMonomial - Class in dev.nm.analysis.function.polynomial

A quadratic monomial has this form: x² + ux + v.

QuadraticMonomial(double, double) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticMonomial

Construct a quadratic monomial.

QuadraticRoot - Class in dev.nm.analysis.function.polynomial.root

This is a solver for finding the roots of a quadratic equation, \(ax^2 + bx + c = 0\).

QuadraticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuadraticRoot

QuadraticSyntheticDivision - Class in dev.nm.analysis.function.polynomial

Divide a polynomial P(x) by a quadratic monomial (x² + ux + v) to give the quotient Q(x) and the remainder (b * (x + u) + a).

QuadraticSyntheticDivision(Polynomial, QuadraticMonomial) - Constructor for class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision

Divide a polynomial by a quadratic monomial.

quant - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

point indices in F[i]; kappa index

Quantile - Class in dev.nm.stat.descriptive.rank

Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable.

Quantile(double[], Quantile.QuantileType) - Constructor for class dev.nm.stat.descriptive.rank.Quantile

Construct a Quantile calculator.

Quantile(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Quantile

Construct a Quantile calculator using the default type: Quantile.QuantileType.APPROXIMATELY_MEDIAN_UNBIASED.

quantile(double) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassQuantile

Gets the quantile at a cumulative probability mass.

quantile(double) - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

Gets the quantile, the inverse of the cumulative distribution function.

quantile(double) - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

Gets the quantile, the inverse of the cumulative distribution function.

quantile(double) - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.FDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

quantile(double) - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the quantile, the inverse of the cumulative distribution function.

quantile(double) - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.TDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

quantile(double) - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

quantile(double) - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

quantile(double) - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

quantile(double) - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

quantile(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Quantile.QuantileType - Enum in dev.nm.stat.descriptive.rank

the available quantile definitions

QuarticRoot - Class in dev.nm.analysis.function.polynomial.root

This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

QuarticRoot(QuarticRoot.QuarticSolver) - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot

Construct a quartic equation solver.

QuarticRoot() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRoot

Construct a quartic equation solver.

QuarticRoot.QuarticSolver - Interface in dev.nm.analysis.function.polynomial.root

This defines a quartic equation solver.

QuarticRootFerrari - Class in dev.nm.analysis.function.polynomial.root

This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using the Ferrari method.

QuarticRootFerrari() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari

QuarticRootFormula - Class in dev.nm.analysis.function.polynomial.root

This is a quartic equation solver that solves \(ax^4 + bx^3 + cx^2 + dx + e = 0\) using a root-finding formula.

QuarticRootFormula() - Constructor for class dev.nm.analysis.function.polynomial.root.QuarticRootFormula

QuasiBinomial - Class in dev.nm.stat.regression.linear.glm.quasi.family

This is the quasi Binomial distribution in GLM.

QuasiBinomial() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial

quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial

quasiDeviance(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution

the quasi-deviance function corresponding to a single observation

quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma

quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian

quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian

quasiDeviance(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson

QuasiDistribution - Interface in dev.nm.stat.regression.linear.glm.quasi.family

This interface represents the quasi-distribution used in GLM.

QuasiFamily - Class in dev.nm.stat.regression.linear.glm.quasi.family

This interface represents the quasi-family used in GLM.

QuasiFamily(QuasiDistribution, LinkFunction) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

QuasiFamily(QuasiBinomial) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

Construct a Binomial quasi-family.

QuasiFamily(QuasiGamma) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

Construct a Gamma quasi-family.

QuasiFamily(QuasiGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

Construct a Gaussian quasi-family.

QuasiFamily(QuasiInverseGaussian) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

Construct an Inverse Gaussian quasi-family.

QuasiFamily(QuasiPoisson) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiFamily

Construct a Poisson quasi-family.

QuasiGamma - Class in dev.nm.stat.regression.linear.glm.quasi.family

This is the quasi Gamma distribution in GLM.

QuasiGamma() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma

QuasiGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family

This is the quasi Gaussian distribution in GLM.

QuasiGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian

QuasiGLMBeta - Class in dev.nm.stat.regression.linear.glm.quasi

This is the estimate of beta, β^, in a quasi Generalized Linear Model, i.e., a GLM with a quasi-family of distributions.

QuasiGLMBeta(QuasiGLMNewtonRaphson, GLMResiduals) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMBeta

Construct an instance of Beta.

QuasiGLMNewtonRaphson - Class in dev.nm.stat.regression.linear.glm.quasi

The Newton-Raphson method is an iterative algorithm to estimate the β of the quasi GLM regression.

QuasiGLMNewtonRaphson(double, int) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson

Constructs an instance to run the Newton-Raphson method.

QuasiGLMProblem - Class in dev.nm.stat.regression.linear.glm.quasi

This class represents a quasi generalized linear regression problem.

QuasiGLMProblem(Vector, Matrix, boolean, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem

Constructs a quasi GLM problem.

QuasiGLMProblem(LMProblem, QuasiFamily) - Constructor for class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMProblem

Constructs a quasi GLM problem from a linear regression problem.

QuasiGLMResiduals - Class in dev.nm.stat.regression.linear.glm.quasi

Residual analysis of the results of a quasi Generalized Linear Model regression.

QuasiInverseGaussian - Class in dev.nm.stat.regression.linear.glm.quasi.family

This is the quasi Inverse-Gaussian distribution in GLM.

QuasiInverseGaussian() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian

quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiBinomial

quasiLikelihood(double, double) - Method in interface dev.nm.stat.regression.linear.glm.quasi.family.QuasiDistribution

the quasi-likelihood function corresponding to a single observation Q(μ; y)

quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGamma

quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiGaussian

quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiInverseGaussian

quasiLikelihood(double, double) - Method in class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson

QuasiMinimalResidualSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary

The Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system.

QuasiMinimalResidualSolver(PreconditionerFactory, PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver

Construct a Quasi-Minimal Residual (QMR) solver.

QuasiMinimalResidualSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver

Construct a Quasi-Minimal Residual (QMR) solver.

QuasiNewtonMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

The Quasi-Newton methods in optimization are for finding local maxima and minima of functions.

QuasiNewtonMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer

Construct a multivariate minimizer using a Quasi-Newton method.

QuasiNewtonMinimizer.QuasiNewtonImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

This is an implementation of the Quasi-Newton algorithm.

QuasiPoisson - Class in dev.nm.stat.regression.linear.glm.quasi.family

This is the quasi Poisson distribution in GLM.

QuasiPoisson() - Constructor for class dev.nm.stat.regression.linear.glm.quasi.family.QuasiPoisson

QuEST - Class in dev.nm.stat.covariance.nlshrink.quest

QuEST is a function that generates sample eigenvalues from population eigenvalues.

QuEST(Vector, int, double, double) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST

QuEST(Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.quest.QuEST

QuEST.Result - Class in dev.nm.stat.covariance.nlshrink.quest

quotient() - Method in class dev.nm.analysis.function.polynomial.HornerScheme

Get the quotient, Q(x).

quotient() - Method in class dev.nm.analysis.function.polynomial.QuadraticSyntheticDivision

Get the quotient Q(x).

R

R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

R() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

R() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get the upper triangular matrix R in the QR decomposition, A = QR.

R(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D

Gets the source (or sink) value at a given time t and a position x.

r - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot

the pivot row

r - Variable in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

r(CointegrationMLE, double) - Method in class dev.nm.stat.cointegration.JohansenTest

Get the (most likely) order of cointegration.

R1Projection - Class in dev.nm.analysis.function.rn2r1

Projection creates a real-valued function RealScalarFunction from a vector-valued function RealVectorFunction by taking only one of its coordinate components in the vector output.

R1Projection(RealVectorFunction, int) - Constructor for class dev.nm.analysis.function.rn2r1.R1Projection

Construct a \(R^n \rightarrow R\) projection from a \(R^n \rightarrow R^m\) function f.

R1toConstantMatrix - Class in dev.nm.analysis.function.matrix

A constant matrix function maps a real number to a constant matrix: \(R^n \rightarrow A\).

R1toConstantMatrix(Matrix) - Constructor for class dev.nm.analysis.function.matrix.R1toConstantMatrix

Construct a constant matrix function.

R1toMatrix - Class in dev.nm.analysis.function.matrix

This is a function that maps from R¹ to a Matrix space.

R1toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R1toMatrix

R2() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the diagnostic measure: R-squared.

R2toMatrix - Class in dev.nm.analysis.function.matrix

This is a function that maps from R² to a Matrix space.

R2toMatrix() - Constructor for class dev.nm.analysis.function.matrix.R2toMatrix

R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA.Result

Gets R_bar, the average of observations over time per subject.

R_bar() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA.Result

Gets R_bar, the average of observations over time per subject.

RamerDouglasPeucker - Class in dev.nm.geometry.polyline

The Ramer-Douglas-Peucker algorithm simplifies a PolygonalChain by removing vertices which do not affect the shape of the curve to a given tolerance.

RamerDouglasPeucker(double) - Constructor for class dev.nm.geometry.polyline.RamerDouglasPeucker

Create an algorithm instance with a given threshold for the maximum distance between the original chain and a point in the simplified chain.

Rand1Bin - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim

The Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vector (differential mutation); crossover by performing a uniform crossover (discrete recombination).

Rand1Bin(double, double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.Rand1Bin

Construct an instance of Rand1Bin.

Rand1Bin.DeRand1BinCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim

This chromosome defines the Rand-1-Bin rule.

RandomBetaGenerator - Interface in dev.nm.stat.random.rng.univariate.beta

This is a random number generator that generates random deviates according to the Beta distribution.

randomCSRSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random CSRSparseMatrix.

randomDenseMatrix(int, int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random DenseMatrix.

randomDOKSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random DOKSparseMatrix.

RandomExpGenerator - Interface in dev.nm.stat.random.rng.univariate.exp

This is a random number generator that generates random deviates according to the exponential distribution.

RandomGammaGenerator - Interface in dev.nm.stat.random.rng.univariate.gamma

This is a random number generator that generates random deviates according to the Gamma distribution.

randomLILSparseMatrix(int, int, int, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random LILSparseMatrix.

RandomLongGenerator - Interface in dev.nm.stat.random.rng.univariate

A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed.

randomLowerTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random LowerTriangularMatrix.

RandomNumberGenerator - Interface in dev.nm.stat.random.rng.univariate

A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern.

randomPositiveDefiniteMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random symmetric, positive definite matrix.

RandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random

This interface represents a univariate random process a.k.a.

RandomProcess(TimeGrid) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess

Construct a univariate random process.

RandomRealizationGenerator - Interface in dev.nm.stat.stochasticprocess.univariate.random

This interface defines a generator to construct random realizations from a univariate stochastic process.

RandomRealizationOfRandomProcess - Class in dev.nm.stat.stochasticprocess.univariate.random

This class generates random realizations from a random/stochastic process.

RandomRealizationOfRandomProcess(RandomProcess, int, RandomLongGenerator) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

Construct a random realization generator from a random/stochastic process.

RandomRealizationOfRandomProcess(RandomProcess, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

Construct a random realization generator from a random/stochastic process.

RandomRealizationOfRandomProcess(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

Construct a random realization generator from a discrete SDE.

RandomRealizationOfRandomProcess(SDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

Construct a random realization generator from an SDE.

RandomRealizationOfRandomProcess(SDE, int) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

Construct a random realization generator from an SDE.

RandomStandardNormalGenerator - Interface in dev.nm.stat.random.rng.univariate.normal

This is a random number generator that generates random deviates according to the standard Normal distribution.

randomSymmetricMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random SymmetricMatrix.

randomUpperTriangularMatrix(int, RandomNumberGenerator) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a random UpperTriangularMatrix.

RandomVectorGenerator - Interface in dev.nm.stat.random.rng.multivariate

A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution.

RandomWalk - Class in dev.nm.stat.stochasticprocess.univariate.random

This is the Random Walk construction of a stochastic process per SDE specification.

RandomWalk(DiscreteSDE, TimeGrid, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk

Constructs a univariate stochastic process from an SDE.

RandomWalk(DiscreteSDE, double, double) - Constructor for class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk

Constructs a univariate stochastic process from an SDE.

rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

Computes the rank by counting the number of non-zero rows in R.

rank() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

rank() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get the numerical rank of A as computed by the QR decomposition.

rank() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Get the rank of A.

rank(Matrix, double) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Compute the numerical rank of a matrix.

rank(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Compute the numerical rank of a matrix.

rank() - Method in class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Get the rank of this vector space.

rank() - Method in class dev.nm.stat.cointegration.CointegrationMLE

Get the rank of the system, i.e., the number of (real) eigenvalues.

Rank - Class in dev.nm.stat.descriptive.rank

Rank is a relationship between a set of items such that, for any two items, the first is either "ranked higher than", "ranked lower than" or "ranked equal to" the second.

Rank(double[]) - Constructor for class dev.nm.stat.descriptive.rank.Rank

Compute the sample ranks of the values.

Rank(double[], double) - Constructor for class dev.nm.stat.descriptive.rank.Rank

Compute the sample ranks of the values.

Rank(double[], Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank

Compute the sample ranks of the values.

Rank(double[], double, Rank.TiesMethod) - Constructor for class dev.nm.stat.descriptive.rank.Rank

Compute the sample ranks of the values.

rank(int) - Method in class dev.nm.stat.descriptive.rank.Rank

Get the rank of the i-th element.

Rank.TiesMethod - Enum in dev.nm.stat.descriptive.rank

The method for assigning ranks when some values are equal (called 'ties').

RankOneMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.quasinewton

The Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.

RankOneMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.RankOneMinimizer

Construct a multivariate minimizer using the Rank One method.

ranks() - Method in class dev.nm.stat.descriptive.rank.Rank

Get the ranks of the values.

Rastrigin - Class in dev.nm.analysis.function.special

The Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms.

Rastrigin(int) - Constructor for class dev.nm.analysis.function.special.Rastrigin

Constructs a Rastrigin function.

rate0 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

rate1 - Variable in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

ratioTest(SimplexTable, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.NaiveRule

This is pivot row selection (Ratio test) rule.

ratioTest(SimplexTable, int) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting

This is pivot row selection (Ratio test) rule.

RayleighDistribution - Class in dev.nm.stat.distribution.univariate

The L2 norm of (x1, x2), where x_i's are normal, uncorrelated, equal variance and have the Rayleigh distributions.

RayleighDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.RayleighDistribution

Construct a Rayleigh distribution.

RayleighRNG - Class in dev.nm.stat.random.rng.univariate

This random number generator samples from the Rayleigh distribution using the inverse transform sampling method.

RayleighRNG(double) - Constructor for class dev.nm.stat.random.rng.univariate.RayleighRNG

Construct a random number generator to sample from the Rayleigh distribution.

rBar() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result

Gets the average correlation \(\bar{r}\).

rbind(Vector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Combines an array of vectors by rows.

rbind(SparseVector...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Combines an array of sparse vectors by rows and returns a sparse matrix.

rbind(List<Vector>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Combines a list of array of vectors by rows.

rbind(Matrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Combines an array of matrices by rows.

rbind(SparseMatrix...) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Combines an array of sparse matrices by rows.

rbinom(int, int, Vector, RandomLongGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Generates n random binomial numbers.

rbinom(int, int, Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Generates n random binomial numbers.

readCSV1d(String) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array.

readCSV1d(String, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array.

readCSV1d(String, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array, with a given separator which overrides the default separator.

readCSV1d(InputStream) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array.

readCSV1d(InputStream, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array.

readCSV1d(InputStream, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils

Read a single-column CSV file (output by write.csv from R) into a 1-D double array, with a given separator which overrides the default separator.

readCSV2d(String) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV file (output by write.csv from R) into a 2-D double array.

readCSV2d(String, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV file (output by write.csv from R) into a 2-D double array.

readCSV2d(String, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV file (output by write.csv from R) into a 2-D double array, with a given separator which overrides the default separator.

readCSV2d(InputStream) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array.

readCSV2d(InputStream, boolean, boolean) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array.

readCSV2d(InputStream, boolean, boolean, String) - Static method in class dev.nm.number.DoubleUtils

Read a multi-column CSV stream (output by write.csv from R) into a 2-D double array, with a given separator which overrides the default separator.

real() - Method in class dev.nm.number.complex.Complex

Get the real part of this complex number.

Real - Class in dev.nm.number

A real number is an arbitrary precision number.

Real(double) - Constructor for class dev.nm.number.Real

Construct a Real from a double.

Real(long) - Constructor for class dev.nm.number.Real

Construct a Real from an integer.

Real(BigDecimal) - Constructor for class dev.nm.number.Real

Construct a Real from a BigDecimal.

Real(BigInteger) - Constructor for class dev.nm.number.Real

Construct a Real from a BigInteger.

Real(String) - Constructor for class dev.nm.number.Real

Construct a Real from a String.

RealInterval - Class in dev.nm.interval

This is an interval on the real line.

RealInterval(Double, Double) - Constructor for class dev.nm.interval.RealInterval

Construct an interval on the real line.

Realization - Interface in dev.nm.stat.timeseries.datastructure.univariate.realtime

This is a univariate time series indexed real numbers.

Realization.Entry - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime

This is the TimeSeries.Entry for a real number -indexed univariate time series.

realizedReturns(int) - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal

Gets the last H-period accumulated realized return.

RealMatrix - Class in dev.nm.algebra.linear.matrix.generic.matrixtype

This is a Real matrix.

RealMatrix(int, int) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

Construct a Real matrix.

RealMatrix(Real[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

Construct a Real matrix.

RealMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

Construct a Real matrix.

RealScalarFunction - Interface in dev.nm.analysis.function.rn2r1

A real valued function a \(R^n \rightarrow R\) function, \(y = f(x_1, ..., x_n)\).

RealScalarFunctionChromosome - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

This chromosome encodes a real valued function.

RealScalarFunctionChromosome(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome

Construct an instance of RealScalarFunctionChromosome.

RealScalarSubFunction - Class in dev.nm.analysis.function.rn2r1

This constructs a RealScalarFunction from another RealScalarFunction by restricting/fixing the values of a subset of variables.

RealScalarSubFunction(RealScalarFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2r1.RealScalarSubFunction

Construct a scalar sub-function.

RealVectorFunction - Interface in dev.nm.analysis.function.rn2rm

A vector-valued function a \(R^n \rightarrow R^m\) function, \([y_1,...,y_m] = f(x_1,...,x_n)\).

RealVectorSpace - Class in dev.nm.algebra.linear.vector.doubles.operation

A vector space is a set of vectors that are closed under some operations.

RealVectorSpace(Matrix, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from a matrix (a set of column vectors).

RealVectorSpace(Matrix) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from a matrix (a set of column vectors).

RealVectorSpace(List<Vector>, double) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from a list of vectors.

RealVectorSpace(List<Vector>) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from a list of vectors.

RealVectorSpace(double, Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from an array of vectors.

RealVectorSpace(Vector...) - Constructor for class dev.nm.algebra.linear.vector.doubles.operation.RealVectorSpace

Construct a vector space from an array of vectors.

RealVectorSubFunction - Class in dev.nm.analysis.function.rn2rm

This constructs a RealVectorFunction from another RealVectorFunction by restricting/fixing the values of a subset of variables.

RealVectorSubFunction(RealVectorFunction, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.rn2rm.RealVectorSubFunction

Construct a vector-valued sub-function.

reciprocal(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the reciprocals of values.

RecursiveGridInterpolation - Class in dev.nm.analysis.curvefit.interpolation.multivariate

This algorithm works by recursively calling lower order interpolation (hence the cost is exponential), until the given univariate algorithm can be used when the remaining dimension becomes one.

RecursiveGridInterpolation(Interpolation) - Constructor for class dev.nm.analysis.curvefit.interpolation.multivariate.RecursiveGridInterpolation

Create an n-dimensional interpolation with a given univariate interpolation algorithm.

reduce(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.Hessenberg

Deprecated.
Not supported yet.

Reference<T> - Class in dev.nm.misc.parallel

Reference() - Constructor for class dev.nm.misc.parallel.Reference

reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4SubVector

reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.Householder4ZeroGenerator

reflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Reflects (or left transform) a range of columns in the underlying matrix with a given Householder.

reflect(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Apply the Householder matrix, H, to a column vector, x.

reflect(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Apply the Householder matrix, H, to a matrix (a set of column vectors), A.

reflectColumns(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

REFLECTION - Static variable in class dev.nm.stat.random.variancereduction.AntitheticVariates

reflectRows(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

reflectVectors(Vector[], int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Apply the Householder matrix, H, to an array of vectors.

relations(Interval<T>) - Method in class dev.nm.interval.Interval

Determine the interval relations between this and Y.

relativeError(double, double) - Static method in class dev.nm.number.DoubleUtils

Compute the relative error for {x1, x0}.

RelativeTolerance - Class in dev.nm.misc.algorithm.iterative.tolerance

The stopping criteria is that the norm of the residual r relative to the input base is equal to or smaller than the specified tolerance, that is, ||r||₂/base ≤ tolerance

RelativeTolerance(double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance

Construct an instance with RelativeTolerance.DEFAULT_TOLERANCE.

RelativeTolerance(double, double) - Constructor for class dev.nm.misc.algorithm.iterative.tolerance.RelativeTolerance

Construct an instance with specified tolerance.

remainder() - Method in class dev.nm.analysis.function.polynomial.HornerScheme

Get the remainder, P(x₀).

REMAINING_ROWS_PCT_THRESHOLD - Static variable in class tech.nmfin.meanreversion.cointegration.RobustCointegration

remove() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Iterator

Overridden to avoid the vector being altered.

remove(Object) - Method in class dev.nm.misc.datastructure.IdentityHashSet

removeActive(int) - Method in class dev.nm.misc.algorithm.ActiveSet

Remove an active index.

removeActiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet

Remove an active constraint by index.

removeAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet

removeContext() - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG

removeEdge(E) - Method in interface dev.nm.graph.Graph

Removes an edge from this graph.

removeEdge(E) - Method in class dev.nm.graph.type.SparseGraph

removeEdge(Arc<V>) - Method in class dev.nm.graph.type.SparseTree

removeEdge(Arc<VertexTree<T>>) - Method in class dev.nm.graph.type.VertexTree

removeInactive(int) - Method in class dev.nm.misc.algorithm.ActiveSet

Remove an inactive index.

removeInactiveByIndex(int) - Method in class dev.nm.misc.algorithm.ActiveSet

Remove an active constraint by index.

removeIsolatedVertices(Graph<V, E>) - Static method in class dev.nm.graph.GraphUtils

Removes those nodes that have no edges from a graph.

removeMaxEdge() - Method in class dev.nm.graph.community.GirvanNewman

Removes the edge with the highest edge-betweeness.

removeVertex(V) - Method in interface dev.nm.graph.Graph

Removes a vertex from this graph.

removeVertex(V) - Method in class dev.nm.graph.type.SparseGraph

removeVertex(V) - Method in class dev.nm.graph.type.SparseTree

removeVertex(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

renameCol(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable

Renames column i.

renameRow(int, Object) - Method in class dev.nm.misc.datastructure.FlexibleTable

Renames row i.

rep(double, int) - Static method in class dev.nm.number.DoubleUtils

Generates an array of doubles of repeated values.

rep(int, int) - Static method in class dev.nm.number.DoubleUtils

Generates an array of ints of repeated values.

replaceInPlace(Matrix, int, int, int, int, Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Replaces a sub-matrix of a matrix with a smaller matrix.

Resampler - Interface in dev.nm.stat.random.sampler.resampler

This is the interface of a re-sampler method.

ResamplerModel - Interface in tech.nmfin.portfoliooptimization.lai2010.fit

residuals() - Method in class dev.nm.stat.regression.linear.glm.GeneralizedLinearModel

Gets the residual analysis of this GLM regression.

residuals() - Method in class dev.nm.stat.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily

Gets the residual analysis.

residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyLARS

residuals() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSObyQP

residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent

residuals() - Method in class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP

residuals() - Method in interface dev.nm.stat.regression.linear.LinearModel

Gets the residual analysis of an OLS regression.

residuals() - Method in class dev.nm.stat.regression.linear.logistic.LogisticRegression

residuals() - Method in class dev.nm.stat.regression.linear.ols.OLSRegression

residuals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the residuals, ε, the differences between sample and fitted values.

residuals - Variable in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity

Result(NonlinearFit.Result, ACERConfidenceInterval, ACERReturnLevel, EmpiricalACER) - Constructor for class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis.Result

retainAll(Collection<?>) - Method in class dev.nm.misc.datastructure.IdentityHashSet

ReturnLevel - Class in dev.nm.stat.evt.function

Given a GEV distribution of a random variable \(X\), the return level \(\eta\) is the value that is expected to be exceeded on average once every interval of time \(T\), with a probability of \(1 / T\).

ReturnLevel(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnLevel

Construct the return level function for a given univariate extreme value distribution.

ReturnLevel(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnLevel

Construct the return level function with the inverse function of a univariate extreme value distribution.

ReturnPeriod - Class in dev.nm.stat.evt.function

The return period \(R\) of a level \(\eta\) for a random variable \(X\) is the mean number of trials that must be done for \(X\) to exceed \(\eta\).

ReturnPeriod(UnivariateEVD) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod

Construct the return period function for a given univariate extreme value distribution.

ReturnPeriod(UnivariateRealFunction) - Constructor for class dev.nm.stat.evt.function.ReturnPeriod

Construct the return period function with the cumulative distribution function of a univariate extreme value distribution.

returns - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

Returns - Class in tech.nmfin.returns

Contains utility methods related to returns computation.

ReturnsCalculator - Interface in tech.nmfin.returns

This interface defines how return is computed from two values of a portfolio.

ReturnsCalculators - Enum in tech.nmfin.returns

Various ways of return calculations.

ReturnsMatrix - Class in tech.nmfin.returns

ReturnsMatrix(Matrix, ReturnsCalculator) - Constructor for class tech.nmfin.returns.ReturnsMatrix

ReturnsMatrix(Matrix) - Constructor for class tech.nmfin.returns.ReturnsMatrix

ReturnsMoments - Class in tech.nmfin.returns.moments

Contains the estimated moments of asset returns.

ReturnsMoments(Vector, Matrix) - Constructor for class tech.nmfin.returns.moments.ReturnsMoments

Constructs an instance.

ReturnsMoments.Estimator - Interface in tech.nmfin.returns.moments

The interface to estimate moments from returns.

ReturnsResamplerFactory - Interface in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler

This is a factory interface to construct new instances of multivariate resamplers.

reverse(T[]) - Static method in class dev.nm.misc.ArrayUtils

Reverse an array in place.

reverse(double...) - Static method in class dev.nm.number.DoubleUtils

Reverse a double array.

reverse(int...) - Static method in class dev.nm.number.DoubleUtils

Reverse an int array.

reverseCopy(double...) - Static method in class dev.nm.number.DoubleUtils

Get a reversed copy of a double array.

reverseCopy(int...) - Static method in class dev.nm.number.DoubleUtils

Get a reversed copy of a int array.

ReversedWeibullDistribution - Class in dev.nm.stat.evt.evd.univariate

The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with \(\xi<0\).

ReversedWeibullDistribution() - Constructor for class dev.nm.stat.evt.evd.univariate.ReversedWeibullDistribution

Create an instance with the default parameter values: location \(\mu=0\), scale \(\sigma=1\), shape \(\alpha=1\).

ReversedWeibullDistribution(double, double, double) - Constructor for class dev.nm.stat.evt.evd.univariate.ReversedWeibullDistribution

Create an instance with the given parameter values.

reverseRange(double[], int, int) - Static method in class dev.nm.number.DoubleUtils

Reverses a range of elements in an array.

rho() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Gets ρ as discussed in the reference.

Ridders - Class in dev.nm.analysis.differentiation

Ridders' method computes the numerical derivative of a function.

Ridders(UnivariateRealFunction, int, double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders

Construct the derivative function of a univariate function using Ridder's method.

Ridders(UnivariateRealFunction, int) - Constructor for class dev.nm.analysis.differentiation.Ridders

Construct the derivative function of a univariate function using Ridder's method.

Ridders(RealScalarFunction, int[], double, int) - Constructor for class dev.nm.analysis.differentiation.Ridders

Construct the derivative function of a vector-valued function using Ridder's method.

Ridders(RealScalarFunction, int[]) - Constructor for class dev.nm.analysis.differentiation.Ridders

Construct the derivative function of a vector-valued function using Ridder's method.

Riemann - Class in dev.nm.analysis.integration.univariate.riemann

This is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method.

Riemann(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann

Construct an integrator.

Riemann() - Constructor for class dev.nm.analysis.integration.univariate.riemann.Riemann

Construct an integrator.

rightConfidenceInterval(double) - Method in class dev.nm.stat.test.mean.T

Get the one sided right confidence interval, [a, ∞)

rightConfidenceInterval(double) - Method in class dev.nm.stat.test.variance.F

Compute the one sided right confidence interval, [a, ∞)

rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Right multiplication by G, namely, A * G.

rightMultiply(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Right multiplication by P.

rightMultiplyInPlace(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Right multiplication by G, namely, A * G.

rightOneSidedPvalue() - Method in class dev.nm.stat.test.mean.T

Get the right, one-sided p-value.

rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.SiegelTukey

Get the right, one-sided p-value.

rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Get the right, one-sided p-value.

rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Compute the one-sided p-value for the statistic greater than a critical value.

rightOneSidedPvalue() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Get the right, one-sided p-value.

rightOneSidedPvalue(double) - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Compute the one-sided p-value for the statistic greater than a critical value.

rightOneSidedPvalue() - Method in class dev.nm.stat.test.variance.F

Get the right, one-sided p-value.

rightReflect(HouseholderInPlace.Householder, int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Reflects (or right transform) a range of rows in the underlying matrix with a given Householder.

rightReflect(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderReflection

Apply the Householder matrix, H, to a matrix (a set of row vectors), A.

rightShift(T[]) - Static method in class dev.nm.misc.ArrayUtils

Get a right shifted array.

rightShift(double...) - Static method in class dev.nm.number.DoubleUtils

Perform a in-memory right-shift (by 1 cell} to an array.

rightShift(double[], int) - Static method in class dev.nm.number.DoubleUtils

Perform a in-memory right-shift (by k cells} to an array.

rightShiftCopy(double...) - Static method in class dev.nm.number.DoubleUtils

Get a right shifted (by 1 cell) copy of an array.

rightShiftCopy(double[], int) - Static method in class dev.nm.number.DoubleUtils

Get a right shifted (by k cells) copy of an array.

Ring<R> - Interface in dev.nm.algebra.structure

A ring is a set R equipped with two binary operations called addition and multiplication: + : R × R → R and ⋅ : R × R → R To qualify as a ring, the set and two operations, (R, +, ⋅), must satisfy the requirements known as the ring axioms.

RNGUtils - Class in dev.nm.stat.random.rng

Provides static methods that wraps random number generators to produce synchronized generators.

rnorm(int, RandomStandardNormalGenerator) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Generates n random standard Normals.

rnorm(int) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Generates n random standard Normals.

RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils

RntoMatrix - Interface in dev.nm.analysis.function.matrix

This interface is a function that maps from Rⁿ to a Matrix space.

RobustAdaptiveMetropolis - Class in dev.nm.stat.random.rng.multivariate.mcmc.metropolis

A variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011).

RobustAdaptiveMetropolis(RealScalarFunction, Matrix, double, double, Vector, RandomStandardNormalGenerator, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis

Constructs a new instance with the given parameters.

RobustAdaptiveMetropolis(RealScalarFunction, double, Vector, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis

Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of 0.5.

RobustCointegration - Class in tech.nmfin.meanreversion.cointegration

This class runs the robust cointegration algorithm on a pair of prices to determine if their cointegration relationship is stable enough to trade.

RobustCointegration() - Constructor for class tech.nmfin.meanreversion.cointegration.RobustCointegration

Romberg - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes

Romberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation.

Romberg(IterativeIntegrator) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Romberg

Extend an integrator using Romberg's method.

root() - Method in interface dev.nm.graph.RootedTree

Gets the root of this tree.

root() - Method in class dev.nm.graph.type.SparseTree

root() - Method in class dev.nm.graph.type.VertexTree

root() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma

ROOT_2 - Static variable in class dev.nm.misc.Constants

\(\sqrt{2}\)

ROOT_2_PI - Static variable in class dev.nm.misc.Constants

\(\sqrt{2\pi}\)

ROOT_PI - Static variable in class dev.nm.misc.Constants

\(\sqrt{\pi}\)

RootedTree<V,E extends Arc<V>> - Interface in dev.nm.graph

A rooted tree is a directed graph, and has a root to measure distance from the root.

rotate(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Deprecated.
Not supported yet.

rotate(V) - Method in class dev.nm.graph.type.SparseTree

This method re-pivots the tree with a new root vertex.

round(double, DoubleUtils.RoundingScheme) - Static method in class dev.nm.number.DoubleUtils

Round up or down a number to an integer.

round(double, int) - Static method in class dev.nm.number.DoubleUtils

Round a number to the precision specified.

round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.AllIntegers

round(Vector) - Method in interface dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.IntegerConstraint

round(Vector) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers

Row(S, String, double...) - Constructor for class dev.nm.stat.regression.linear.panel.PanelData.Row

rowIndices() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray

rowMeans(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the row means.

rowMeanVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the row mean vector of a given matrix.

rows(Matrix, int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Construct a sub-matrix from the rows of a matrix.

rows(Matrix, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the rows of a matrix.

rowSums(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the row sums.

rowSumVector(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the row sum vector of a given matrix.

RSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the diagnostic measure: sum of squared residuals, \(\sum \epsilon^2\).

run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.BlockWinogradAlgorithm

run(SubMatrixBlock, SubMatrixBlock, SubMatrixBlock) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication.DenseMatrixMultiplicationByBlock.BlockAlgorithm

Runs the matrix multiplication task.

run(T) - Method in interface dev.nm.misc.parallel.IterationBody

Execute a (parallel) task.

run(int) - Method in interface dev.nm.misc.parallel.LoopBody

This method contains the code inside the for-loop, as in a native for-loop like this:

run() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

Run the genetic algorithm.

run(double[], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis

Run the analysis with single-period observations.

run(double[][], double) - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERAnalysis

Run the analysis with multi-period observations.

run() - Method in class dev.nm.stat.factor.implicitmodelpca.ExplicitImplicitModelPCA

run() - Method in class dev.nm.stat.factor.implicitmodelpca.ImplicitModelPCA

Runs the regression.

RungeKutta - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

The Runge-Kutta methods are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations.

RungeKutta(RungeKuttaStepper, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta

Constructs a Runge-Kutta algorithm with the given integrator and the constant step size.

RungeKutta(RungeKuttaStepper, int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta

Constructs a Runge-Kutta algorithm with the given integrator and the constant number of steps.

RungeKutta1 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the first-order Runge-Kutta formula, which is the same as the Euler method.

RungeKutta1() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1

RungeKutta10 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the tenth-order Runge-Kutta formula.

RungeKutta10() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10

RungeKutta2 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the second-order Runge-Kutta formula, which can be implemented efficiently with a three-step algorithm.

RungeKutta2() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2

RungeKutta3 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the third-order Runge-Kutta formula.

RungeKutta3() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3

RungeKutta4 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the fourth-order Runge-Kutta formula.

RungeKutta4() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4

RungeKutta5 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the fifth-order Runge-Kutta formula.

RungeKutta5() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5

RungeKutta6 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the sixth-order Runge-Kutta formula.

RungeKutta6() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6

RungeKutta7 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the seventh-order Runge-Kutta formula.

RungeKutta7() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7

RungeKutta8 - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This is the eighth-order Runge-Kutta formula.

RungeKutta8() - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8

RungeKuttaFehlberg - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

The Runge-Kutta-Fehlberg method is a version of the classic Runge-Kutta method, which additionally uses step-size control and hence allows specification of a local truncation error bound.

RungeKuttaFehlberg(double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg

Create a new instance of the Runge-Kutta-Fehlberg method for the given parameters, with the default safety factor value.

RungeKuttaFehlberg(double, double, double) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg

Create a new instance of the Runge-Kutta-Fehlberg method with the given safety factor.

RungeKuttaIntegrator - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

This integrator works with a single-step stepper which estimates the solution for the next step given the solution of the current step.

RungeKuttaIntegrator(RungeKuttaStepper) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaIntegrator

RungeKuttaStepper - Interface in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta

RYDBERG_RINF - Static variable in class dev.nm.misc.PhysicalConstants

The Rydberg constant \(R_{\infty}\) in reciprocal meter (m^-1).

S

s() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Gets the value of s.

S - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath

This is the auxiliary helper to solve the dual problem.

s - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution

This is the auxiliary helper to solve the dual problem.

s - Variable in exception dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.exception.LPUnbounded

This is the pricing column that does not have an eligible row that passes the ratio test, hence the problem is unbounded.

s - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SimplexPivoting.Pivot

the pivot column

S() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem

Gets the original sample covariance matrix.

S() - Method in class dev.nm.stat.covariance.LedoitWolf2004.Result

Gets the sample covariance matrix S.

S() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result

Sample covariance matrix.

s() - Method in class dev.nm.stat.descriptive.rank.Rank

\[ s = \sum(t_i^2 - t_i) \]

S() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis

Gets the covariance (or correlation) matrix.

S() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.RobustAdaptiveMetropolis

Gets the tuned scaling matrix (this changes each time a new sample is drawn).

S_t_hat(int) - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal

Predicts the accumulated H-period return at time t.

sample() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

Get the sample kurtosis (biased estimator).

sample() - Method in class dev.nm.stat.descriptive.moment.Skewness

Get the sample skewness (biased estimator).

SampleAutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.sample

This is the sample Auto-Correlation Function (ACF) for a univariate data set.

SampleAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation

Construct the sample ACF for a time series.

SampleAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation

Construct the sample ACF for a time series.

SampleAutoCovariance - Class in dev.nm.stat.timeseries.linear.univariate.sample

This is the sample Auto-Covariance Function (ACVF) for a univariate data set.

SampleAutoCovariance(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance

Construct the sample ACVF for a time series.

SampleAutoCovariance(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance

Construct the sample ACVF for a time series.

SampleAutoCovariance.Type - Enum in dev.nm.stat.timeseries.linear.univariate.sample

the available auto-covariance types

SampleCovariance - Class in dev.nm.stat.descriptive.covariance

This class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.

SampleCovariance(Matrix) - Constructor for class dev.nm.stat.descriptive.covariance.SampleCovariance

Construct the covariance matrix of a matrix.

SampleCovariance(Matrix, boolean) - Constructor for class dev.nm.stat.descriptive.covariance.SampleCovariance

Construct the covariance matrix of a matrix.

SampleCovarianceEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleCovarianceEstimator

SampleMeanEstimator() - Constructor for class tech.nmfin.portfoliooptimization.PortfolioOptimizationAlgorithm.SampleMeanEstimator

SamplePartialAutoCorrelation - Class in dev.nm.stat.timeseries.linear.univariate.sample

This is the sample partial Auto-Correlation Function (PACF) for a univariate data set.

SamplePartialAutoCorrelation(IntTimeTimeSeries, SampleAutoCovariance.Type) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation

Construct the sample PACF for a time series.

SamplePartialAutoCorrelation(IntTimeTimeSeries) - Constructor for class dev.nm.stat.timeseries.linear.univariate.sample.SamplePartialAutoCorrelation

Construct the sample PACF for a time series.

scale() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.Pow

Get the exponential of the coefficient.

scale(double[], double) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Scale each element in an array by a multiplier.

scale() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the scalings applied to each variable.

scale() - Method in class dev.nm.stat.factor.pca.PCAbyEigen

Gets the scalings applied to each variable.

scale() - Method in class dev.nm.stat.factor.pca.PCAbySVD

scaleColumn(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Scale a column: A[, j] = c * A[, j]

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

scaled(double) - Method in interface dev.nm.algebra.linear.matrix.doubles.Matrix

Scale this matrix, A, by a constant.

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

Multiply the elements in this by a scalar, element-by-element.

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

scaled(MatrixAccess, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation

c * A

scaled(MatrixAccess, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

scaled(Real) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

scaled(double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

scaled(Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

scaled(F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

scaled(Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

scaled(Vector, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Scales a vector, element-by-element.

scaled(Vector, Real) - Method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Scales a vector, element-by-element.

scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

scaled(double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

scaled(Real) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

scaled(double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Scale this vector by a constant, entry-by-entry.

scaled(Real) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Scale this vector by a constant, entry-by-entry.

scaled(F) - Method in interface dev.nm.algebra.structure.VectorSpace

× : F × V → V
The result of applying this function to a scalar, c, in F and v in V is denoted cv.

scaled(Real) - Method in class dev.nm.analysis.function.polynomial.Polynomial

scaled(double) - Method in class dev.nm.analysis.function.polynomial.Polynomial

scaled(double[], double) - Method in class dev.nm.number.doublearray.CompositeDoubleArrayOperation

scaled(double[], double) - Method in interface dev.nm.number.doublearray.DoubleArrayOperation

Scale each entry of a double array.

scaled(double[], double) - Method in class dev.nm.number.doublearray.ParallelDoubleArrayOperation

scaled(double[], double) - Method in class dev.nm.number.doublearray.SimpleDoubleArrayOperation

scaledAlpha(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure

Gets the scaled forward probabilities at time t.

scaledBeta(int) - Method in class dev.nm.stat.hmm.ForwardBackwardProcedure

Gets the scaled backward probabilities at time t.

scaledBetas() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSFitting.Estimators

Gets the entire sequence of estimated (LARS) regression coefficients, scaled by the L2 norm of each row.

ScaledPolynomial - Class in dev.nm.analysis.function.polynomial

This constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow.

ScaledPolynomial(Polynomial, double) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial

Construct a scaled polynomial, with a base of the scaling factor.

ScaledPolynomial(Polynomial) - Constructor for class dev.nm.analysis.function.polynomial.ScaledPolynomial

Construct a scaled polynomial, with 2 as the base of the scaling factor.

scaleRow(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Scale a row: A[i, ] = c * A[i, ]

ScientificNotation - Class in dev.nm.number

Scientific notation expresses a number in this form x = a * 10^b a is called the significand or mantissa, and 1 ≤ |a| < 10.

ScientificNotation(double, int) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of a number in this form: x = a * 10^b.

ScientificNotation(BigDecimal, int) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of a number in this form: x = a * 10^b.

ScientificNotation(BigDecimal) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of a number.

ScientificNotation(BigInteger) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of an integer.

ScientificNotation(long) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of a long.

ScientificNotation(double) - Constructor for class dev.nm.number.ScientificNotation

Construct the scientific notation of a double.

scores() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Gets the matrix of scores, computed using either Thompson's (1951) scores, or Bartlett's (1937) weighted least-squares scores.

scores() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the scores of supplied data on the principal components.

scoringRule() - Method in class dev.nm.stat.factor.factoranalysis.FactorAnalysis

Gets the scoring rule.

SDE - Class in dev.nm.stat.stochasticprocess.univariate.sde

This class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of the following form.

SDE(Drift, Diffusion) - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.SDE

Construct a univariate diffusion type stochastic differential equation.

SDPDualProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem

A dual SDP problem, as in equation 14.4 in the reference, takes the following form.

SDPDualProblem(Vector, SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem

Constructs a dual SDP problem.

SDPDualProblem.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem

This is the collection of equality constraints: \[ \sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0} \]

SDPPrimalProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.problem

A Primal SDP problem, as in equation 14.1 in the reference, takes the following form.

SDPPrimalProblem(SymmetricMatrix, SymmetricMatrix[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPPrimalProblem

Constructs a primal SDP problem.

sdPrincipalComponent(int) - Method in interface dev.nm.stat.factor.pca.PCA

Gets the standard deviation of the i-th principal component.

sdPrincipalComponents() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).

sdPrincipalComponents() - Method in class dev.nm.stat.factor.pca.PCAbyEigen

Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).

sdPrincipalComponents() - Method in class dev.nm.stat.factor.pca.PCAbySVD

Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix, though the calculation is actually done with the singular values of the data matrix)

SDPT3v4 - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint

This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4.

search(Vector...) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution

search(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound

search(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod

Search for a solution that optimizes the objective function from the given starting points.

search(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

Search for a solution that optimizes the objective function from the given starting points.

search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

Searches for a solution that optimizes the objective function from the default starting points.

search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

Search for a solution that optimizes the objective function from the given starting points.

search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution

search(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

search(CentralPath) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

Search for a solution that optimizes the objective function from the given starting points.

search(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

search(PrimalDualSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

Searches for a solution that optimizes the objective function from the given starting point.

search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

Searches for a solution that optimizes the objective function from the starting point given by K.

search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

Searches for a minimizer for the quadratic programming problem.

search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

search() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

Searches for a minimizer for the quadratic programming problem.

search(Vector) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

Searches for a minimizer for the quadratic programming problem.

search(QPSolution) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

Searches for a minimizer for the quadratic programming problem from the given starting points.

search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

search(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution

search(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

Search for a solution that optimizes the objective function from the given starting points.

search(Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

Search for a solution that minimizes the objective function from the given starting point.

search(Vector, Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

Search for a solution that minimizes the objective function from the given starting point.

search(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer.Solution

search(Vector) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer.Solution

search(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution

Perform a Nelder-Mead search from an initial simplex.

search(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

search(double, double, double) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution

search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution

search(double, double) - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer.Solution

search(double, double, double) - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution

Search for a minimum within the interval [lower, upper].

search(double, double) - Method in class dev.nm.solver.univariate.GridSearchMinimizer.Solution

search(double, double, double) - Method in interface dev.nm.solver.univariate.UnivariateMinimizer.Solution

Search for a minimum within the interval [lower, upper].

search(double, double) - Method in interface dev.nm.solver.univariate.UnivariateMinimizer.Solution

Search for a minimum within the interval [lower, upper].

search(int) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher

Performs a search for parameters for a given id.

search() - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneTwisterParamSearcher

Performs a search for parameters with no id.

search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer

search(Ceta, double, double) - Method in interface tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer

Searches the maximal point of a given C(η) function within a given range.

search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer

search(Ceta, double, double) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer

sec(double) - Static method in class dev.nm.geometry.TrigMath

Returns the secant of an angle.

sech(double) - Static method in class dev.nm.geometry.TrigMath

Returns the hyperbolic secant of a hyperbolic angle.

seed(long...) - Method in class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1

Seed the random number generator to produce repeatable sequences.

seed(long...) - Method in class dev.nm.stat.distribution.discrete.ProbabilityMassSampler

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic

seed(long...) - Method in class dev.nm.stat.evt.evd.univariate.rng.InverseTransformSamplingEVDRNG

seed(long...) - Method in class dev.nm.stat.evt.markovchain.ExtremeValueMC

seed(long...) - Method in class dev.nm.stat.evt.timeseries.MARMASim

seed(long...) - Method in class dev.nm.stat.hmm.HMMRNG

seed(long...) - Method in class dev.nm.stat.markovchain.SimpleMC

seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedGenerator

seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRLG

Delegate to the underlying random long generator.

seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRNG

Delegate to the underlying random number generator.

seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.cache.ConcurrentCachedRVG

Delegate to the underlying random vector generator.

seed(long...) - Method in class dev.nm.stat.random.rng.concurrent.context.ContextRNG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.BurnInRVG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.HypersphereRVG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.IID

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.ErgodicHybridMCMC

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.metropolis.AbstractMetropolis

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.MultinomialRVG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.NormalRVG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.ThinRVG

seed(long...) - Method in class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox

SEED - Static variable in class dev.nm.stat.random.rng.RNGUtils

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BernoulliTrial

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.Cheng1978

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969

Deprecated.

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BinomialRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.BurnInRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.KunduGupta2007

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.MarsagliaTsang2000

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.InverseTransformSampling

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.LogNormalRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.BoxMuller

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.ConcurrentStandardNormalRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.MarsagliaBray1964

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.NormalRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.truncated.InverseTransformSamplingTruncatedNormalRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.normal.Zignor2005

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.poisson.Knuth1969

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.ThinRNG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.CompositeLinearCongruentialGenerator

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.LEcuyer

Seed the random number/vector/scenario generator to produce repeatable experiments.

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.Lehmer

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.linear.MRG

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.mersennetwister.MersenneTwister

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.MWC8222

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR0

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.SHR3

seed(long...) - Method in class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009

seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject

seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacement

seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.bootstrap.CaseResamplingReplacementForObject

seed(long...) - Method in class dev.nm.stat.random.sampler.resampler.multivariate.GroupResampler

seed(long...) - Method in interface dev.nm.stat.random.Seedable

Seed the random number/vector/scenario generator to produce repeatable experiments.

seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateBrownianRRG

seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomRealizationOfRandomProcess

seed(long...) - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomWalk

seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess

seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomRealizationOfRandomProcess

seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomWalk

seed(long...) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUSim

seed(long...) - Method in class dev.nm.stat.test.distribution.pearson.AS159

seed(long...) - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim

seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMASim

seed(long...) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHSim

seed(long...) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GroupResamplerFactory

Seedable - Interface in dev.nm.stat.random

A seed-able experiment allow the same experiment to be repeated in exactly the same way.

select(double[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils

Select the array elements which satisfy the boolean test.

select(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils

Select the array elements which satisfy the boolean test.

select(GLMProblem, Matrix, int[]) - Method in interface dev.nm.stat.regression.linear.glm.modelselection.ForwardSelection.Step

select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC

select(GLMProblem, Matrix, int[]) - Method in class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue

SelectionByAIC - Class in dev.nm.stat.regression.linear.glm.modelselection

In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC.

SelectionByAIC() - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.SelectionByAIC

SelectionByZValue - Class in dev.nm.stat.regression.linear.glm.modelselection

In each step, the most significant factor is added, until all remaining factors are insignificant.

SelectionByZValue(double) - Constructor for class dev.nm.stat.regression.linear.glm.modelselection.SelectionByZValue

Creates an instance with the given significance level [0, 1].

SemiImplicitExtrapolation - Class in dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation

Semi-Implicit Extrapolation is a method of solving ordinary differential equations, that is similar to Burlisch-Stoer extrapolation.

SemiImplicitExtrapolation(double, int) - Constructor for class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation

Create an instance of this algorithm with the given precision parameter and the maximum number of iterations allowed.

seq(int, double, double) - Static method in class dev.nm.number.DoubleUtils

Generate a sequence of double values with a given start value and a given constant increment.

seq(double, double, double) - Static method in class dev.nm.number.DoubleUtils

Generates a sequence of doubles from from up to to with increments inc.

seq(double, double, int) - Static method in class dev.nm.number.DoubleUtils

Generate a sequence of n equi-spaced double values, from start to end (inclusive).

seq(int, int, int) - Static method in class dev.nm.number.DoubleUtils

Generates a sequence of ints from from up to to with increments inc.

seq(int, int) - Static method in class dev.nm.number.DoubleUtils

Generates a sequence of ints from from up to to with increments 1.

Sequence - Interface in dev.nm.analysis.sequence

A sequence is an ordered list of (real) numbers.

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

set(int, int, double) - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixAccess

Set the matrix entry at [i,j] to a value.

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Deprecated.
GivensMatrix is immutable

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Deprecated.
use the swap functions instead

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

set(int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Deprecated.

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

set(int, int, double) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

Deprecated.
SubMatrixRef is immutable

set(int, int, F) - Method in interface dev.nm.algebra.linear.matrix.generic.GenericMatrixAccess

Set the matrix entry at [i,j] to a value.

set(int, int, Complex) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

set(int, int, F) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

set(int, int, Real) - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef

Deprecated.

set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

set(int, DenseVector) - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

Replaces a sub-vector v[from : replacement.length] by a replacement starting at position from.

set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

This method is overridden to always throw VectorAccessException.

set(int, double) - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

Deprecated.

set(int, double) - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Change the value of an entry in this vector.

set(int, int, double) - Method in class dev.nm.misc.datastructure.FlexibleTable

set(T, int...) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray

set(T, int...) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection

Replaces the element at the specified position in this list with the specified element.

set(int, int) - Method in class dev.nm.misc.datastructure.SortableArray

set(T) - Method in class dev.nm.misc.parallel.Reference

set(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

Associate this variation to a particular general constrained minimization problem with only equality constraints.

set(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1

Associate this variation to a particular general constrained minimization problem.

setCachingCriticalLine(boolean) - Method in class tech.nmfin.portfoliooptimization.clm.MCLNiedermayer

Sets the algorithm to compute and cache the whole critical line, so that optimal weights can be computed as quick as a linear search for turning points on the line.

setColumn(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

Set the values for a column in the matrix, i.e., [*, j].

setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

Set the values for a column in the matrix, i.e., [*, j].

setColumn(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

Changes the matrix column values to a vector value.

setContext(long) - Method in class dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG

Sets the context of this thread.

setDeltaT(double) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.AbstractHybridMCMC

Sets the value of dt that will be used in the subsequent iterations.

setDomain(List<Vector>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer.Solution

Gives the domain values for the brute force search to try.

setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Set the current time differential.

setDt(double) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft

Set the current time differential.

setDt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt

setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt

setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt

setFt(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.FiltrationFunction

Set the filtration for this function.

setInitials(BBNode...) - Method in class dev.nm.misc.algorithm.bb.BranchAndBound

setInitials(S...) - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod

Supply the starting points for the search.

setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

setInitials(CentralPath...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

setInitials(PrimalDualSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

No need to set initials for the dual problem.

setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

setInitials(QPSolution...) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution

setInitials(Vector...) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

Supply the starting points for the search.

setInitials(Vector...) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution

setInitials(Vector...) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

setLicenseFile(File) - Static method in class dev.nm.misc.license.License

Overrides the default license file.

setLicenseKey(String) - Static method in class dev.nm.misc.license.License

Sets the license key for this invocation.

setParent(BFS.Node<V>) - Method in class dev.nm.graph.algorithm.traversal.BFS.Node

setParent(V) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

Sets the parent of the node.

setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory

setPopulation(List<Chromosome>) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptimCellFactory

Set the current generation.

setRiskAversionCoefficient(double) - Method in class tech.nmfin.portfoliooptimization.markowitz.MarkowitzByQP

Sets the risk aversion coefficient, effectively moving along the efficient frontier.

setRow(int, double...) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

Set the values for a row in the matrix, i.e., [i, *].

setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

Set the values for a row in the matrix, i.e., [i, *].

setRow(int, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

Changes the matrix row values to a vector value.

setVisitTime(int) - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

setXt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Set the current value of the stochastic process.

setXt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft

Set the current value of the stochastic process.

setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Set the current value of the Gaussian innovation.

setZt(Vector) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft

Set the current value of the Gaussian innovation.

setZt(double) - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt

ShapiroWilk - Class in dev.nm.stat.test.distribution.normality

The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population.

ShapiroWilk(double[]) - Constructor for class dev.nm.stat.test.distribution.normality.ShapiroWilk

Perform the Shapiro-Wilk test to test for the null hypothesis that a sample comes from a normally distributed population.

ShapiroWilkDistribution - Class in dev.nm.stat.test.distribution.normality

Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population.

ShapiroWilkDistribution(int) - Constructor for class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Construct a Shapiro-Wilk distribution.

shellsort(double...) - Static method in class dev.nm.number.DoubleUtils

Sort an array using Shell sort.

ShortestPath<V> - Interface in dev.nm.graph.algorithm.shortestpath

In graph theory, a shortest path algorithm finds a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized.

shouldUseParallel(int) - Static method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky.CholeskyBanachiewiczParallelized

SHR0 - Class in dev.nm.stat.random.rng.univariate.uniform

SHR0 is a simple uniform random number generator.

SHR0() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR0

SHR3 - Class in dev.nm.stat.random.rng.univariate.uniform

SHR3 is a 3-shift-register generator with period 2^32-1.

SHR3() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.SHR3

shutdown() - Method in class dev.nm.misc.parallel.ParallelExecutor

Shuts down the executor gracefully.

SiegelTukey - Class in dev.nm.stat.test.rank

The Siegel-Tukey test tests for differences in scale (variability) between two groups.

SiegelTukey(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.SiegelTukey

Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.

SiegelTukey(double[], double[], double) - Constructor for class dev.nm.stat.test.rank.SiegelTukey

Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.

SiegelTukey(double[], double[]) - Constructor for class dev.nm.stat.test.rank.SiegelTukey

Perform the Siegel-Tukey test to test for differences in scale (variability) between two groups.

sigma() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

sigma(double, double) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D

Gets the diffusion coefficient at a given time t and a position x.

sigma - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.PortfolioRiskExactSigma

Sigma() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint

sigma - Variable in class dev.nm.stat.hmm.mixture.distribution.NormalMixtureDistribution.Lambda

the standard deviation

sigma() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateSDE

Get the diffusion matrix: \(\sigma(t, X_t, Z_t, ...)\).

sigma() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

sigma() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess

Get the volatility.

sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get the white noise covariance matrix.

sigma() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Get the white noise covariance matrix.

sigma() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the white noise variance.

sigma - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.ModelParam

sigma() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

Gets the diffusion volatility.

sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.armagarch.AR1GARCH11Model

Computes the conditional variance based on the past information.

sigma2(double, double) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCH11Model

Computes the conditional variance based on the past information.

sigma2(double[], double[]) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

Compute the conditional variance based on the past information.

sigma2 - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2

sigma2() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel

Gets the conditional variances of residuals over time.

sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit

sigma2() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit

sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2

Deprecated.

sigma_ij(int, int) - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.DiffusionSigma

Get the F_t adapted function the D[i,j] entry in the diffusion matrix.

sign() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Gets the sign of the permutation matrix which is also the determinant.

significand() - Method in class dev.nm.number.ScientificNotation

Get the significand.

signum(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the signs of values.

SimilarMatrix - Class in dev.nm.algebra.linear.matrix.doubles.operation

Given a matrix A and an invertible matrix P, we construct the similar matrix B s.t., B = P^-1AP

SimilarMatrix(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SimilarMatrix

Constructs the similar matrix B = P^-1AP.

simObs(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Simulates a sequence of observations per the model specification.

SimpleAnnealingFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction

This annealing function takes a random step in a uniform direction, where the step size depends only on the temperature.

SimpleAnnealingFunction(RandomVectorGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction.SimpleAnnealingFunction

SimpleAR1Fit - Class in tech.nmfin.portfoliooptimization.lai2010.fit

This class does a quick AR(1) fitting to the time series, essentially treating the returns as independent.

SimpleAR1Fit(Matrix) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit

SimpleAR1Moments - Class in tech.nmfin.portfoliooptimization.lai2010.fit

SimpleAR1Moments() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Moments

SimpleArc<V> - Class in dev.nm.graph.type

A simple arc has two vertices: head and tail.

SimpleArc(V, V, double) - Constructor for class dev.nm.graph.type.SimpleArc

Construct a simple arc.

SimpleArc(V, V) - Constructor for class dev.nm.graph.type.SimpleArc

Construct a simple arc.

SimpleCell(RealScalarFunction, Vector) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory.SimpleCell

SimpleCellFactory - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

A SimpleCellFactory produces SimpleCellFactory.SimpleCells.

SimpleCellFactory(double, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory

Construct an instance of a SimpleCellFactory.

SimpleCellFactory.SimpleCell - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

A SimpleCell implements the two genetic operations.

SimpleDoubleArrayOperation - Class in dev.nm.number.doublearray

This is a simple, single-threaded implementation of the array math operations.

SimpleDoubleArrayOperation() - Constructor for class dev.nm.number.doublearray.SimpleDoubleArrayOperation

SimpleEdge<V> - Class in dev.nm.graph.type

A simple edge has two vertices.

SimpleEdge(V, V, double) - Constructor for class dev.nm.graph.type.SimpleEdge

Construct a simple edge.

SimpleEdge(V, V) - Constructor for class dev.nm.graph.type.SimpleEdge

Construct a simple edge.

SimpleGARCHFit - Class in tech.nmfin.portfoliooptimization.lai2010.fit

This class does a quick GARCH(1,1) fitting to the time series, essentially treating the returns as independent.

SimpleGARCHFit(Matrix) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit

SimpleGARCHMoments1 - Class in tech.nmfin.portfoliooptimization.lai2010.fit

Estimates the moments by GARCH model.

SimpleGARCHMoments1() - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments1

SimpleGARCHMoments2 - Class in tech.nmfin.portfoliooptimization.lai2010.fit

Estimates the moments by GARCH model.

SimpleGARCHMoments2(GARCHResamplerFactory2) - Constructor for class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHMoments2

SimpleGridMinimizer - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

This minimizer is a simple global optimization method.

SimpleGridMinimizer(SimpleGridMinimizer.NewCellFactoryCtor, RandomLongGenerator, double, int, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

Construct a SimpleGridMinimizer to solve unconstrained minimization problems.

SimpleGridMinimizer(RandomLongGenerator, double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

Construct a SimpleGridMinimizer to solve unconstrained minimization problems.

SimpleGridMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

Construct a SimpleGridMinimizer to solve unconstrained minimization problems.

SimpleGridMinimizer.NewCellFactoryCtor - Interface in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

This factory constructs a new SimpleCellFactory for each minimization problem.

SimpleGridMinimizer.Solution - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid

This is the solution to a minimization problem using SimpleGridMinimizer.

SimpleMatrixMathOperation - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation

This is a generic, single-threaded implementation of matrix math operations.

SimpleMatrixMathOperation() - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

SimpleMC - Class in dev.nm.stat.markovchain

This is a time-homogeneous Markov chain with a finite state space.

SimpleMC(Vector, Matrix) - Constructor for class dev.nm.stat.markovchain.SimpleMC

Constructs a time-homogeneous Markov chain with a finite state space.

SimpleTemperatureFunction - Class in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction

Abstract class for the common case where \(T^V_t = T^A_t\).

SimpleTemperatureFunction() - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction

SimpleTimeSeries - Class in dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime

This simple univariate time series simply wraps a double[] to form a time series.

SimpleTimeSeries(double[]) - Constructor for class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

Constructs an instance of SimpleTimeSeries.

SimplexCuttingPlaneMinimizer - Class in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane

The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory.

SimplexCuttingPlaneMinimizer(LPSimplexSolver, SimplexCuttingPlaneMinimizer.CutterFactory) - Constructor for class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer

Construct a cutting-plane minimizer to solve an MILP problem.

SimplexCuttingPlaneMinimizer.CutterFactory - Interface in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane

This factory constructs a new Cutter for each MILP problem.

SimplexCuttingPlaneMinimizer.CutterFactory.Cutter - Interface in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane

A Cutter defines how to cut a simplex table, i.e., how to relax a linear program so that the current non-integer solution is no longer feasible to the relaxation.

SimplexPivoting - Interface in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting

A simplex pivoting finds a row and column to exchange to reduce the cost function.

SimplexPivoting.Pivot - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting

the pivot

SimplexTable - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex

This is a simplex table used to solve a linear programming problem using a simplex method.

SimplexTable(LPCanonicalProblem1, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Construct a simplex table from a canonical linear programming problem.

SimplexTable(LPCanonicalProblem1) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Construct a simplex table from a canonical linear programming problem.

SimplexTable(LPProblem, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Construct a simplex table from a general linear programming problem.

SimplexTable(LPProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Construct a simplex table from a general linear programming problem.

SimplexTable(SimplexTable) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Copy constructor.

SimplexTable.Label - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex

SimplexTable.LabelType - Enum in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex

simplify(PolygonalChain) - Method in class dev.nm.geometry.polyline.RamerDouglasPeucker

Simplify the given polygonal chain.

Simpson - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes

Simpson's rule can be thought of as a special case of Romberg's method.

Simpson(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Simpson

Construct an integrator that implements Simpson's rule.

simStates(int) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Simulates a sequence of states per the model specification.

SimulatedAnnealingMinimizer - Class in dev.nm.solver.multivariate.unconstrained.annealing

Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy.

SimulatedAnnealingMinimizer(TemperatureFunction, AnnealingFunction, TemperedAcceptanceProbabilityFunction, int, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer

Constructs a new instance.

SimulatedAnnealingMinimizer(int, double, StopCondition, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer

Constructs a new instance to use BoltzTemperatureFunction, BoltzAnnealingFunction and MetropolisAcceptanceProbabilityFunction.

sin(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the sine of a vector, element-by-element.

sin(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Sine of a complex number.

sinc(double) - Static method in class dev.nm.geometry.TrigMath

Returns the unnormalized sinc function of an angle.

SingularValueByDQDS - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds

Computes all the singular values of a bidiagonal matrix.

SingularValueByDQDS(BidiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds.SingularValueByDQDS

Computes all the singular values of a bidiagonal matrix.

sinh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Hyperbolic sine of a complex number.

size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen

Get the number of distinct eigenvalues.

size() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.LDDecomposition

size() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Gets the number of variables in the linear system.

size() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

size() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef

size() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

size() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

size() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

size() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Get the length of this vector.

size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid

size(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid

Get the size of the grid in the given dimension x_i.

size(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

size() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction

size() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs

Get the number of points.

size() - Method in class dev.nm.analysis.function.tuple.PartialFunction

size() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs

size() - Method in class dev.nm.interval.Intervals

Get the number of disjoint intervals.

size() - Method in class dev.nm.misc.datastructure.IdentityHashSet

size(int) - Method in class dev.nm.misc.datastructure.MultiDimensionalArray

size(int) - Method in interface dev.nm.misc.datastructure.MultiDimensionalCollection

Returns the size of the collection along the given dimension.

size() - Method in interface dev.nm.solver.multivariate.constrained.constraint.Constraints

Get the number of constraints.

size() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralConstraints

size() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints

size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.problem.SDPDualProblem.EqualityConstraints

size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem.EqualityConstraints

size() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints

Gets the number of constraints.

size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateDLMSeries

size() - Method in class dev.nm.stat.dlm.multivariate.MultivariateLinearKalmanFilter

Get T, the number of hidden states or observations.

size() - Method in class dev.nm.stat.dlm.univariate.DLMSeries

size() - Method in class dev.nm.stat.dlm.univariate.LinearKalmanFilter

Get T, the number of hidden states or observations.

size - Variable in class dev.nm.stat.hmm.mixture.distribution.BinomialMixtureDistribution.Lambda

the size

size() - Method in class dev.nm.stat.regression.linear.panel.PanelData

Get the number of rows in the panel.

size() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid

size() - Method in interface dev.nm.stat.stochasticprocess.timegrid.TimeGrid

Get the number of time points.

size() - Method in class dev.nm.stat.stochasticprocess.timegrid.UnitGrid

size() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration

Get the length of the history.

size() - Method in class dev.nm.stat.timeseries.datastructure.DateTimeGenericTimeSeries

size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

size() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

size() - Method in interface dev.nm.stat.timeseries.datastructure.TimeSeries

Get the length of the time series.

size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries

size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

size() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries

sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

sizeX() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Define the size of the grid along the x-axis.

sizeX() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

sizeY() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Define the size of the grid along the y-axis.

sizeY() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

Sk - Variable in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.QuasiNewtonMinimizer.QuasiNewtonImpl

This is the approximate inverse of the Hessian matrix.

skew() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.FDistribution

Gets the skewness of this distribution.

skew() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

skew() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the skewness of this distribution.

skew() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.TDistribution

Gets the skewness of this distribution.

skew() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

skew() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

skew() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

skew() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

skew() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

skew() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

skew() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

skew() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Deprecated.

skew() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Deprecated.

Skewness - Class in dev.nm.stat.descriptive.moment

Skewness is a measure of the asymmetry of the probability distribution.

Skewness() - Constructor for class dev.nm.stat.descriptive.moment.Skewness

Construct an empty Skewness calculator.

Skewness(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Skewness

Construct a Skewness calculator, initialized with a sample.

Skewness(Skewness) - Constructor for class dev.nm.stat.descriptive.moment.Skewness

Copy constructor.

SmallestSubscriptRule - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting

Bland's smallest-subscript rule is for anti-cycling in choosing a pivot.

SmallestSubscriptRule() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting.SmallestSubscriptRule

SOCPDualProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem

This is the Dual Second Order Conic Programming problem.

SOCPDualProblem(Vector, Matrix[], Vector[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

Constructs a dual SOCP problem.

SOCPDualProblem(SOCPDualProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPDualProblem

Copy constructor.

SOCPDualProblem.EqualityConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem

SOCPGeneralConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem

This represents the SOCP general constraint of this form.

SOCPGeneralConstraint(Matrix, Vector, Vector, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraint

Constructs a SOCP general constraint.

SOCPGeneralConstraints - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem

This represents a set of SOCP general constraints of this form.

SOCPGeneralConstraints() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints

Constructs a set of SOCP general constraints.

SOCPGeneralConstraints(List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints

Constructs a set of SOCP general constraints.

SOCPGeneralConstraints(SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralConstraints

Constructs a set of SOCP general constraints.

SOCPGeneralProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem

Many convex programming problems can be represented in the following form.

SOCPGeneralProblem(SOCPGeneralProblem) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem

Copy constructor.

SOCPGeneralProblem(Vector, SOCPGeneralConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem

Construct a general Second Order Conic Programming problem.

SOCPGeneralProblem(Vector, List<SOCPGeneralConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.SOCPGeneralProblem

Construct a general Second Order Conic Programming problem.

SOCPMaximumLoan - Class in tech.nmfin.portfoliooptimization.socp.constraints

Transforms a maximum loan constraint into the compact SOCP form.

SOCPMaximumLoan(Vector, Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan

Constructs a maximum loan constraint.

SOCPMaximumLoan(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPMaximumLoan

Constructs a maximum loan constraint.

SOCPNoTradingList1 - Class in tech.nmfin.portfoliooptimization.socp.constraints

Transforms a black list (not to trade a new position) constraint into the compact SOCP form.

SOCPNoTradingList1(Vector, Matrix, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1

Constructs a black list constraint.

SOCPNoTradingList1(Vector, Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1

Constructs a black list constraint.

SOCPNoTradingList1(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPNoTradingList1

Constructs a black list constraint.

SOCPNoTradingList2 - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar

Transforms a black list (not to trade a new position) constraint into the compact SOCP form.

SOCPNoTradingList2(Vector, Matrix, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2

Constructs a black list constraint.

SOCPNoTradingList2(Vector, Matrix) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPNoTradingList2

Constructs a black list constraint.

SOCPPortfolioConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

An SOCP constraint for portfolio optimization, e.g., market impact, is represented by a set of constraints in this form.

SOCPPortfolioConstraint() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint

SOCPPortfolioConstraint.ConstraintViolationException - Exception in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Exception thrown when a constraint is violated.

SOCPPortfolioConstraint.Variable - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

the variables involved in SOCPGeneralConstraints

SOCPPortfolioObjectiveFunction - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Constructs the objective function for portfolio optimization.

SOCPPortfolioObjectiveFunction(Matrix, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction

Constructs the objective function for an SOCP portfolio optimization (minimization) problem.

SOCPPortfolioObjectiveFunction(Vector, double[], SOCPRiskConstraint, SOCPPortfolioConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction

Constructs the objective function for an SOCP portfolio optimization (minimization) problem.

SOCPPortfolioObjectiveFunction(Matrix, double, SOCPRiskConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction

Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term.

SOCPPortfolioObjectiveFunction(Vector, double, SOCPRiskConstraint) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioObjectiveFunction

Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term.

SOCPPortfolioProblem - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

Constructs an SOCP problem for portfolio optimization.

SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, SOCPPortfolioConstraint[]) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem

Constructs an SOCP problem for portfolio optimization.

SOCPPortfolioProblem(SOCPPortfolioObjectiveFunction, Collection<SOCPPortfolioConstraint>) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioProblem

Constructs an SOCP problem for portfolio optimization.

SOCPRiskConstraint - Class in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization

SOCPRiskConstraint() - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPRiskConstraint

SOCPSectorExposure - Class in tech.nmfin.portfoliooptimization.socp.constraints.ybar

Transforms a sector exposure constraint into the compact SOCP form.

SOCPSectorExposure(Vector, Vector[], Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure

Constructs a sector exposure constraint.

SOCPSectorExposure(Vector, Vector[], Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.ybar.SOCPSectorExposure

Constructs a sector exposure constraint.

SOCPSectorNeutrality - Class in tech.nmfin.portfoliooptimization.socp.constraints

Transforms a sector neutral constraint into the compact SOCP form.

SOCPSectorNeutrality(Vector, Vector[], double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality

Constructs a sector neutral constraint.

SOCPSectorNeutrality(Vector, Vector[]) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSectorNeutrality

Constructs a sector neutral constraint.

SOCPSelfFinancing - Class in tech.nmfin.portfoliooptimization.socp.constraints

Transforms a self financing constraint into the compact SOCP form.

SOCPSelfFinancing(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing

Constructs a zero value constraint.

SOCPSelfFinancing(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPSelfFinancing

Constructs a zero value constraint.

SOCPZeroValue - Class in tech.nmfin.portfoliooptimization.socp.constraints

Transforms a zero value constraint into the compact SOCP form.

SOCPZeroValue(Vector, double) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue

Constructs a zero value constraint.

SOCPZeroValue(Vector) - Constructor for class tech.nmfin.portfoliooptimization.socp.constraints.SOCPZeroValue

Constructs a zero value constraint.

solution() - Method in interface dev.nm.misc.algorithm.bb.BBNode

the solution to the sub-problem associated with this node

Solution(PrimalDualPathFollowingMinimizer, SDPDualProblem, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution

Solves the semi-definite programming problem using the Homogeneous Self-Dual Path-Following algorithm.

Solution(SDPDualProblem, double, double) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

Solution(RealScalarFunction, RealVectorFunction, EqualityConstraints, GreaterThanConstraints) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

solution() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode

Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim.Solution

Solution(RealScalarFunction) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

Solution(UnivariateRealFunction) - Constructor for class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

solve(UpperTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution

Solve Ux = b.

solve(UpperTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution

solve(UpperTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution

solve(UpperTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.BackwardSubstitution

solve(LowerTriangularMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution

Solve Lx = b.

solve(LowerTriangularMatrix, Vector, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution

solve(LowerTriangularMatrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution

solve(LowerTriangularMatrix, Matrix, double) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ForwardSubstitution

solve(Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LinearSystemSolver

Get a particular solution for the linear system, Ax = b.

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver

Solve Ax = b.

solve(Matrix, Matrix) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LUSolver

Solves AX = B.

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverByQR

In the ordinary least square sense, solve Ax = y

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.OLSSolverBySVD

In the ordinary least square sense, solve Ax = y

solve(TridiagonalMatrix, Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm

Solves a tridiagonal matrix equation.

solve(LSProblem, IterationMonitor<Vector>) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver

Solves iteratively Ax = b until the solution converges, i.e., the norm of residual (b - Ax) is less than or equal to the threshold.

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedConjugateResidualSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.GeneralizedMinimalResidualSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.MinimalResidualSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver

solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner

Return the input vector x.

solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner

Return P^-1x, where P is the diagonal matrix of A.

solve(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner

Solve Mv = x, where M is the preconditioner matrix.

solve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner

Solve Mz = x using this SSOR preconditioner.

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.GaussSeidelSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.JacobiSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver

solve(LSProblem) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver

solve(LSProblem, IterationMonitor<Vector>) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver

solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.BurlischStoerExtrapolation

Perform the extrapolation.

solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation

solve(ODE1stOrderWith2ndDerivative) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.extrapolation.SemiImplicitExtrapolation

solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton.AdamsBashforthMoulton

Solve an ODE using the Adams-Bashforth-Moulton method.

solve(ODE1stOrder) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolver

Solves the given ODE problem.

solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta

Solves a first order ODE.

solve(ODE1stOrder) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaFehlberg

Solve the given ODE using Runge-Kutta-Fehlberg method.

solve(PoissonEquation2D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2.IterativeCentralDifference

Solve a Poisson's equation problem, with the given grid resolution parameters.

solve(WaveEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.ExplicitCentralDifference1D

Solve an one-dimensional wave equation, with the resolution parameters of the solution grid.

solve(WaveEquation2D, int, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.ExplicitCentralDifference2D

Solve a two-dimensional wave equation, with the resolution parameters of the solution grid.

solve(ConvectionDiffusionEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.CrankNicolsonConvectionDiffusionEquation1D

Solves a 1 dimensional convection-diffusion equation.

solve(HeatEquation1D, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.CrankNicolsonHeatEquation1D

Solves the given one-dimensional heat equation.

solve(HeatEquation2D, int, int, int) - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.AlternatingDirectionImplicitMethod

Solve the given two-dimensional heat equation problem, with the given numbers of points along the three axes in the grid (time, x, and y).

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.CubicRoot

Solve \(ax^3 + bx^2 + cx + d = 0\).

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.jenkinstraub.JenkinsTraubReal

Solve a polynomial equation.

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.LinearRoot

Solve ax + b = 0.

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.PolyRoot

Get the roots/zeros of a polynomial.

solve(Polynomial) - Method in interface dev.nm.analysis.function.polynomial.root.PolyRootSolver

solve(Polynomial, double) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot

Solve \(ax^2 + bx + c = 0\).

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuadraticRoot

solve(double, double, double, double, double) - Method in interface dev.nm.analysis.function.polynomial.root.QuarticRoot.QuarticSolver

Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRoot

Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

solve(double, double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari

Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFerrari

Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

solve(double, double, double, double, double) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula

solve(Polynomial) - Method in class dev.nm.analysis.function.polynomial.root.QuarticRootFormula

Solve \(ax^4 + bx^3 + cx^2 + dx + e = 0\).

solve(RealVectorFunction, Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot

Searches for a root, x such that f(x) = 0.

solve(RealScalarFunction[], Vector) - Method in class dev.nm.analysis.root.multivariate.NewtonSystemRoot

Searches for a root, x such that f(x) = 0.

solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BisectionRoot

solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.BrentRoot

solve(UnivariateRealFunction, double, double) - Method in class dev.nm.analysis.root.univariate.BrentRoot

solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.HalleyRoot

solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.HalleyRoot

solve(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.HalleyRoot

Search for a root, x, in the interval [lower, upper] such that f(x) = 0.

solve(UnivariateRealFunction, double, double, double...) - Method in class dev.nm.analysis.root.univariate.NewtonRoot

solve(UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.NewtonRoot

solve(UnivariateRealFunction, UnivariateRealFunction, double) - Method in class dev.nm.analysis.root.univariate.NewtonRoot

Searches for a root, x, in the interval [lower, upper] such that f(x) = 0.

solve(UnivariateRealFunction, double, double, double...) - Method in interface dev.nm.analysis.root.univariate.Uniroot

Search for a root, x, in the interval [lower, upper] such that f(x) = 0.

solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer

solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer

solve(SDPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer

solve(SOCPDualProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer

solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2

solve(LPCanonicalProblem1) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.FerrisMangasarianWrightPhase2

solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver

solve(LPRevisedSimplexSolver.Problem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver

solve(SimplexTable) - Method in interface dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPSimplexSolver

Solve an LP problem by a simplex algorithm on a simplex table

solve(SimplexTable) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver

solve(LPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPTwoPhaseSolver

solve(QuadraticFunction, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer

Solves an unconstrained quadratic programming problem of this form.

solve(QuadraticFunction) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer

Solves an unconstrained quadratic programming problem of this form.

solve(QuadraticFunction, LinearEqualityConstraints, double) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer

Solves a quadratic programming problem subject to equality constraints.

solve(QuadraticFunction, LinearEqualityConstraints) - Static method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.QPSimpleMinimizer

Solves a quadratic programming problem subject to equality constraints.

solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer

solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer

solve(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer

solve(BoxOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.box.BoxGeneralizedSimulatedAnnealingMinimizer

solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.penaltymethod.PenaltyMethodMinimizer

solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer

solve(RealScalarFunction, EqualityConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer

Minimize a function subject to only equality constraints.

solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer

solve(RealScalarFunction, RealVectorFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

Minimize a function subject to only inequality constraints.

solve(RealScalarFunction, GreaterThanConstraints) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

Minimize a function subject to only inequality constraints.

solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

solve(BruteForceIPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.bruteforce.BruteForceIPMinimizer

solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPBranchAndBoundMinimizer

solve(ILPProblem) - Method in class dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane.SimplexCuttingPlaneMinimizer

solve(ConstrainedOptimProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

Solves a constrained optimization sub-problem that is already in the form of a ConstrainedOptimProblem.

solve(ConstrainedOptimSubProblem) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

solve(ConstrainedOptimProblem, Map<Integer, Double>) - Method in class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

Solves a constrained sub-problem by specifying the fixing explicitly.

solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.DEOptim

solve(OptimProblem) - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

solve(MinMaxProblem<T>) - Method in class dev.nm.solver.multivariate.minmax.LeastPth

solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.SimulatedAnnealingMinimizer

solve(Function<Vector, R>) - Method in class dev.nm.solver.multivariate.unconstrained.BruteForceMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ConjugateGradientMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.FletcherReevesMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.PowellMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.IterativeC2Maximizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.linesearch.FletcherLineSearch

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.HuangMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer.MySteepestDescent

solve(RealVectorFunction, RntoMatrix) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer

Solve the minimization problem to minimize F = vf' * vf.

solve(RealVectorFunction) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.GaussNewtonMinimizer

Solve the minimization problem to minimize F = vf' * vf.

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.NewtonRaphsonMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer

Solve a minimization problem with a C² objective function.

solve(OptimProblem) - Method in class dev.nm.solver.multivariate.unconstrained.DoubleBruteForceMinimizer

solve(P) - Method in interface dev.nm.solver.Optimizer

Solve an optimization problem, e.g., OptimProblem.

solve(UnivariateRealFunction) - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer

Minimize a univariate function.

solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer

solve(C2OptimProblem) - Method in class dev.nm.solver.univariate.GridSearchMinimizer

solve(UnivariateRealFunction) - Method in class dev.nm.solver.univariate.GridSearchMinimizer

Minimizes a univariate function.

solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.BrentCetaMaximizer

solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CombinedCetaMaximizer

solve(Ceta) - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.GridSearchCetaMaximizer

solve() - Method in class tech.nmfin.trend.dai2011.Dai2011Solver

Solves the HJB equations (a nonlinear PDE system) to get the optimal stopping regions, i.e., the buying region (buy threshold) and the selling region (sell threshold).

SomeIntegers(IPProblem) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.IntegralConstrainedCellFactory.SomeIntegers

Construct the integral constraint from an Integer Programming problem.

SORSweep - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary

This is a building block for SOR and SSOR to perform the forward or backward sweep.

SORSweep(Matrix, Vector, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SORSweep

Construct an instance to perform forward or backward sweep for a linear system Ax = b.

SortableArray - Class in dev.nm.misc.datastructure

These arrays can be sorted according to the dictionary order.

SortableArray(int) - Constructor for class dev.nm.misc.datastructure.SortableArray

SortableArray(int...) - Constructor for class dev.nm.misc.datastructure.SortableArray

SortableArray(List<Integer>) - Constructor for class dev.nm.misc.datastructure.SortableArray

SortedOrderedPairs - Class in dev.nm.analysis.function.tuple

The ordered pairs are first sorted by abscissa, then by ordinate.

SortedOrderedPairs(OrderedPairs) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs

SortedOrderedPairs(double[], double[]) - Constructor for class dev.nm.analysis.function.tuple.SortedOrderedPairs

sortInColumnOrder(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts a list of sparse matrix entries in column order (row indices in the same row can be in arbitrary order) in linear time.

sortInColumnOrder(SparseMatrix.Entry[], int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts an array of sparse matrix entries in column order (row indices in the same row can be in arbitrary order) in linear time.

sortInRowColumnOrder(List<SparseMatrix.Entry>, int, int, boolean, boolean) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts a list of sparse matrix entries in row-column order in linear time.

sortInRowColumnOrder(SparseMatrix.Entry[], int, int, boolean, boolean) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts an array of sparse matrix entries in row-column order in linear time.

sortInRowOrder(List<SparseMatrix.Entry>, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts a list of sparse matrix entries in row order (column indices in the same row can be in arbitrary order) in linear time.

sortInRowOrder(SparseMatrix.Entry[], int) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Sorts an array of sparse matrix entries in row order (column indices in the same row can be in arbitrary order) in linear time.

SparseDAGraph<V,E extends Arc<V>> - Class in dev.nm.graph.type

This class implements the sparse directed acyclic graph representation.

SparseDAGraph(boolean) - Constructor for class dev.nm.graph.type.SparseDAGraph

Construct an empty directed acyclic graph.

SparseDAGraph() - Constructor for class dev.nm.graph.type.SparseDAGraph

Construct an empty directed acyclic graph.

SparseDAGraph(DAGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDAGraph

(Copy) construct a directed acyclic graph from another directed acyclic graph.

SparseDiGraph<V,E extends Arc<V>> - Class in dev.nm.graph.type

This class implements the sparse directed graph representation.

SparseDiGraph() - Constructor for class dev.nm.graph.type.SparseDiGraph

Construct an empty graph.

SparseDiGraph(DiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseDiGraph

(Copy) construct a graph from another graph.

SparseGraph<V,E extends HyperEdge<V>> - Class in dev.nm.graph.type

This class implements the sparse graph representation.

SparseGraph() - Constructor for class dev.nm.graph.type.SparseGraph

Construct an empty graph.

SparseGraph(Graph<V, E>) - Constructor for class dev.nm.graph.type.SparseGraph

(Copy) construct a graph from another graph.

SparseMatrix - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

A sparse matrix stores only non-zero values.

SparseMatrix.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This is a (non-zero) entry in a sparse matrix.

SparseMatrix.Entry.TopLeftFirstComparator - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns).

SparseMatrix.ValueArray - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

SparseMatrixUtils - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

These are the utility functions for SparseMatrix.

SparseStructure - Interface in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This interface defines common operations on sparse structures such as sparse vector or sparse matrix.

SparseTree<V> - Class in dev.nm.graph.type

This class implements the sparse tree representation.

SparseTree(V) - Constructor for class dev.nm.graph.type.SparseTree

Construct a tree with a root.

SparseUnDiGraph<V,E extends UndirectedEdge<V>> - Class in dev.nm.graph.type

This class implements the sparse undirected graph representation.

SparseUnDiGraph() - Constructor for class dev.nm.graph.type.SparseUnDiGraph

Construct an empty graph.

SparseUnDiGraph(UnDiGraph<V, E>) - Constructor for class dev.nm.graph.type.SparseUnDiGraph

(Copy) construct a graph from another graph.

SparseVector - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

A sparse vector stores only non-zero values.

SparseVector(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Constructs a sparse vector.

SparseVector(int, int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Constructs a sparse vector.

SparseVector(int, List<SparseVector.Entry>) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Constructs a sparse vector.

SparseVector(double...) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Constructs a sparse vector from a double[].

SparseVector(Vector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Constructs a sparse vector from a vector.

SparseVector(SparseVector) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

Copy constructor.

SparseVector.Entry - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This is an entry in a SparseVector.

SparseVector.Entry.Comparator - Enum in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns).

SparseVector.Iterator - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse

This wrapper class overrides the Iterator.remove() method to throw an exception when called.

SpearmanRankCorrelation - Class in dev.nm.stat.descriptive.correlation

Spearman's rank correlation coefficient or Spearman's rho is a non-parametric measure of statistical dependence between two variables.

SpearmanRankCorrelation(double[], double[]) - Constructor for class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation

Construct a Spearman rank calculator initialized with two samples.

spectralDensity(double) - Method in interface dev.nm.stat.evt.evd.bivariate.BivariateEVD

The density \(h\) of the spectral measure \(H\) on the interval (0,1).

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricLogistic

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricMixed

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDAsymmetricNegativeLogistic

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDBilogistic

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDColesTawn

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDHuslerReiss

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDLogistic

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeBilogistic

spectralDensity(double) - Method in class dev.nm.stat.evt.evd.bivariate.BivariateEVDNegativeLogistic

Spectrum - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.eigen

A spectrum is the set of eigenvalues of a matrix.

spread - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

spread() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

S = A - bB

SQPActiveSetMinimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset

Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization.

SQPActiveSetMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.

SQPActiveSetMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.

SQPActiveSetMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with inequality constraints.

SQPActiveSetMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This is the solution to a general minimization with only inequality constraints using the SQP Active Set algorithm.

SQPActiveSetMinimizer.VariationFactory - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This factory constructs a new instance of SQPASVariation for each SQP problem.

SQPActiveSetOnlyEqualityConstraint1Minimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This implementation is a modified version of Algorithm 15.1 in the reference to solve a general constrained optimization problem with only equality constraints.

SQPActiveSetOnlyEqualityConstraint1Minimizer(SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer

Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.

SQPActiveSetOnlyEqualityConstraint1Minimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint1Minimizer

Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.

SQPActiveSetOnlyEqualityConstraint1Minimizer.VariationFactory - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This factory constructs a new instance of SQPASEVariation for each SQP problem.

SQPActiveSetOnlyEqualityConstraint2Minimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2.

SQPActiveSetOnlyEqualityConstraint2Minimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer

Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.

SQPActiveSetOnlyEqualityConstraint2Minimizer(double, double, int, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPActiveSetOnlyEqualityConstraint2Minimizer

Construct an SQP Active Set minimizer to solve general minimization problems with equality constraints.

SQPActiveSetOnlyInequalityConstraintMinimizer - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This implementation is a modified version of Algorithm 15.2 in the reference to solve a general constrained optimization problem with only inequality constraints.

SQPActiveSetOnlyInequalityConstraintMinimizer(SQPActiveSetMinimizer.VariationFactory, double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.

SQPActiveSetOnlyInequalityConstraintMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.

SQPActiveSetOnlyInequalityConstraintMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetOnlyInequalityConstraintMinimizer

Construct an SQP Active Set minimizer to solve general minimization problems with only inequality constraints.

SQPActiveSetOnlyInequalityConstraintMinimizer.Solution - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This is the solution to a general minimization problem with only inequality constraints using the SQP Active Set algorithm.

SQPASEVariation - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints using Sequential Quadratic Programming.

SQPASEVariation1 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.

SQPASEVariation1(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

Construct a variation.

SQPASEVariation1() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

Construct a variation.

SQPASEVariation2 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint

This implementation tries to find an exact positive definite Hessian whenever possible.

SQPASEVariation2(double, double, int) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2

Construct a variation.

SQPASEVariation2() - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2

Construct a variation.

SQPASVariation - Interface in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming.

SQPASVariation1 - Class in dev.nm.solver.multivariate.constrained.general.sqp.activeset

This implementation is a modified version of Algorithm 15.4 in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming.

SQPASVariation1(double) - Constructor for class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1

Construct a variation.

sqrt(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the square roots of a vector, element-by-element.

sqrt(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Square root of a complex number.

sqrt(double[]) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the square roots of values.

squared(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the squares of a vector, element-by-element.

squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

Get the square Q matrix.

squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

squareQ() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

squareQ() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get the square Q matrix.

SSORPreconditioner - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner

SSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed as A = D + L + L^t The SSOR preconditioning matrix is defined as M = (D + L)D^-1(D + L)^t or, parameterized by ω M(ω) = (1/(2 - ω))(D / ω + L)(D / ω)^-1(D / ω + L)^t

SSORPreconditioner(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner

Construct an SSOR preconditioner with a symmetric coefficient matrix.

StandardCumulativeNormal - Interface in dev.nm.analysis.function.special.gaussian

The cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval \((-\infty, x]\).

standardDeviation() - Method in class dev.nm.stat.descriptive.moment.Variance

Get the standard deviation of the sample, which is the square root of the variance.

standardError() - Method in class dev.nm.stat.evt.evd.univariate.fitting.EstimateByLogLikelihood

Get the standard errors of the fitted parameters.

StandardInterval - Class in dev.nm.analysis.integration.univariate.riemann.substitution

This transformation is for mapping integral region from [a, b] to [-1, 1].

StandardInterval(double, double) - Constructor for class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval

Construct a StandardInterval substitution rule.

standardized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

standard residual = residual / v1 / sqrt(RSS / (n-m))

StandardNormalRNG - Class in dev.nm.stat.random.rng.univariate.normal

An alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution.

StandardNormalRNG() - Constructor for class dev.nm.stat.random.rng.univariate.normal.StandardNormalRNG

standarizedInnovations() - Method in interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel

Gets the standarized innovations (normalized by the conditional standard deviation at the time) of the time series.

standarizedInnovations() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit

standarizedInnovations() - Method in class tech.nmfin.portfoliooptimization.lai2010.fit.SimpleGARCHFit

state - Variable in class dev.nm.stat.dlm.multivariate.MultivariateDLMSim.Innovation

the simulated state

state - Variable in class dev.nm.stat.dlm.univariate.DLMSim.Innovation

the simulated state

state() - Method in class dev.nm.stat.hmm.HmmInnovation

Get the hidden state.

state() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Gets the current system configuration.

state() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

UP trend is -ve; DOWN trend is +ve.

StateEquation - Class in dev.nm.stat.dlm.univariate

This is the state equation in a controlled dynamic linear model.

StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Construct a state equation.

StateEquation(UnivariateRealFunction, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Construct a state equation.

StateEquation(UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Construct a state equation without control variables.

StateEquation(double, double, double, RandomStandardNormalGenerator) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Construct a time-invariant state equation.

StateEquation(double, double) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Construct a time-invariant state equation without control variables.

StateEquation(StateEquation) - Constructor for class dev.nm.stat.dlm.univariate.StateEquation

Copy constructor.

Statistic - Interface in dev.nm.stat.descriptive

A statistic (singular) is a single measure of some attribute of a sample (e.g., its arithmetic mean value).

StatisticFactory - Interface in dev.nm.stat.descriptive

A factory to construct a new Statistic.

statistics() - Method in class dev.nm.stat.factor.factoranalysis.FAEstimator

Get the test statistics of the factor analysis.

statistics() - Method in class dev.nm.stat.test.distribution.AndersonDarling

statistics() - Method in class dev.nm.stat.test.distribution.CramerVonMises2Samples

statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov1Sample

statistics() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov2Samples

statistics() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

statistics() - Method in class dev.nm.stat.test.distribution.normality.JarqueBera

statistics() - Method in class dev.nm.stat.test.distribution.normality.Lilliefors

statistics() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilk

statistics() - Method in class dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest

statistics() - Method in class dev.nm.stat.test.HypothesisTest

Get the test statistics.

statistics() - Method in class dev.nm.stat.test.mean.OneWayANOVA

statistics() - Method in class dev.nm.stat.test.mean.T

statistics() - Method in class dev.nm.stat.test.rank.KruskalWallis

statistics() - Method in class dev.nm.stat.test.rank.SiegelTukey

statistics() - Method in class dev.nm.stat.test.rank.VanDerWaerden

statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

statistics() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.BreuschPagan

statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.Heteroskedasticity

statistics() - Method in class dev.nm.stat.test.regression.linear.heteroskedasticity.White

statistics() - Method in class dev.nm.stat.test.timeseries.adf.AugmentedDickeyFuller

statistics() - Method in class dev.nm.stat.test.timeseries.portmanteau.BoxPierce

Deprecated.

statistics() - Method in class dev.nm.stat.test.variance.Bartlett

statistics() - Method in class dev.nm.stat.test.variance.BrownForsythe

statistics() - Method in class dev.nm.stat.test.variance.F

statistics() - Method in class dev.nm.stat.test.variance.Levene

statisticsAlternative() - Method in class dev.nm.stat.test.distribution.AndersonDarling

Gets the alternative Anderson-Darling statistic (adjusted for ties).

stderr() - Method in class dev.nm.stat.regression.linear.LMBeta

Gets the standard errors of the coefficients β^.

stderr() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the standard error of the residuals.

stderr() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit

Get the asymptotic standard errors of the estimators.

stderr() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares

Get the asymptotic standard errors of the estimated parameters, φ and θ.

stdev() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix

Gets the standard deviations of the elements.

stdev(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix

Gets the standard deviation of the i-th element.

stdev() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

stdev(double) - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

Gets the stdev of the last % portion in the spread.

stdev() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

Gets the stdev of the spread.

stdinno - Variable in class tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler.GARCHResamplerFactory2

SteepestDescentImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

SteepestDescentMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent

A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction.

SteepestDescentMinimizer(LineSearch, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer

Construct a multivariate minimizer using a steepest descent method.

SteepestDescentMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer

Construct a multivariate minimizer using a steepest descent method.

SteepestDescentMinimizer.SteepestDescentImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent

This is an implementation of the steepest descent method.

SteepestDescentSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary

The Steepest Descent method (SDM) solves a symmetric n-by-n linear system.

SteepestDescentSolver(PreconditionerFactory, int, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver

Construct a Steepest Descent method (SDM) solver.

SteepestDescentSolver(int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver

Construct a Steepest Descent method (SDM) solver.

STEFAN_BOLTZMANN_SIGMA - Static variable in class dev.nm.misc.PhysicalConstants

The Stegan-Boltzmann constant \(\sigma\) in (W m^-2 K^-4).

step() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeLinearSystemSolver.Solution

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta1

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta10

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta2

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta3

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta4

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta5

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta6

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta7

step(DerivativeFunction, Vector, double, double, double) - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKutta8

step(DerivativeFunction, Vector, double, double, double) - Method in interface dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta.RungeKuttaStepper

step() - Method in class dev.nm.misc.algorithm.bb.BranchAndBound

step() - Method in interface dev.nm.misc.algorithm.iterative.IterativeMethod

Do the next iteration.

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

Do the next iteration.

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualInteriorPointMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPDualActiveSetMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset.QPPrimalActiveSetMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPbySOCPMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPActiveSetMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

Run a step in genetic algorithm: produce the next generation of chromosome pool.

step() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.NelderMeadMinimizer.Solution

step() - Method in class dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.SteepestDescentMinimizer.SteepestDescentImpl

step() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Performs one step of the leap-frogging algorithm.

StepFunction - Class in dev.nm.analysis.function.rn2r1.univariate

A step function (or staircase function) is a finite linear combination of indicator functions of intervals.

StepFunction(double) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction

Construct an empty step function.

StepFunction(OrderedPairs, double) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction

Construct a step function from a collection ordered pairs.

StepFunction(OrderedPairs) - Constructor for class dev.nm.analysis.function.rn2r1.univariate.StepFunction

Construct a step function from a collection ordered pairs.

steps(int) - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Performs n steps of the leap-frogging algorithm.

StopCondition - Interface in dev.nm.misc.algorithm.stopcondition

Defines when an algorithm stops (the iterations).

StringUtils - Class in dev.nm.misc

Utility methods for string manipulation.

studentized() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))

SturmCount - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Computes the Sturm count, the number of negative pivots encountered while factoring tridiagonal T - σ I = LDL^T.

SturmCount(LDDecomposition, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SturmCount

Creates an instance for computing the Sturm count of a given robust representation (T - σ I = LDL^T).

subA() - Method in class dev.nm.stat.regression.linear.glm.modelselection.GLMModelSelection

Constructs a covariates subset.

subarray(double[], int[]) - Static method in class dev.nm.number.DoubleUtils

Get a sub-array of the original array with the given indices.

subarray(int[], int[]) - Static method in class dev.nm.number.DoubleUtils

Get a sub-array of the original array with the given indices.

subDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets the sub-diagonal of a matrix as a vector.

subDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets the sub-diagonal of a sparse matrix as a sparse vector.

SubFunction<R> - Class in dev.nm.analysis.function

A sub-function, g, is defined over a subset of the domain of another (original) function, f.

SubFunction(Function<Vector, R>, Map<Integer, Double>) - Constructor for class dev.nm.analysis.function.SubFunction

Constructs a sub-function.

subject() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row

Gets the subject of the row.

subMatrix(Matrix, int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the four corners of a matrix.

subMatrix(SparseMatrix, int, int, int, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the four corners of a sparse matrix.

subMatrix(Matrix, List<Integer>, List<Integer>) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the intersections of rows and columns of a matrix.

subMatrix(Matrix, int[], int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the intersections of rows and columns of a matrix.

subMatrix(SparseMatrix, int[], int[]) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixFactory

Constructs a sub-matrix from the intersections of rows and columns of a sparse matrix.

SubMatrixBlock - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication

Sub-matrix block representation for block algorithm.

SubMatrixRef - Class in dev.nm.algebra.linear.matrix.doubles.operation

This is a 'reference' to a sub-matrix of a larger matrix without copying it.

SubMatrixRef(Matrix, int[], int[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

Constructs a sub-matrix reference.

SubMatrixRef(Matrix, int, int, int, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

Constructs a sub-matrix reference.

SubMatrixRef(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

Constructs a reference to the whole matrix.

SubProblemMinimizer - Class in dev.nm.solver.multivariate.constrained

This minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem.

SubProblemMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem, IterativeSolution<Vector>>>) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

SubProblemMinimizer(double, int) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

SubProblemMinimizer(double) - Constructor for class dev.nm.solver.multivariate.constrained.SubProblemMinimizer

SubProblemMinimizer.ConstrainedMinimizerFactory>> - Interface in dev.nm.solver.multivariate.constrained

This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.

SubProblemMinimizer.IterativeSolution<Vector> - Interface in dev.nm.solver.multivariate.constrained

SubstitutionRule - Interface in dev.nm.analysis.integration.univariate.riemann.substitution

A substitution rule specifies \(x(t)\) and \(\frac{\mathrm{d} x}{\mathrm{d} t}\).

subTree(V) - Method in interface dev.nm.graph.RootedTree

Gets a sub-tree starting from a vertex.

subTree(V) - Method in class dev.nm.graph.type.SparseTree

subTree(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

subVector(Vector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets a sub-vector from a vector.

subVector(SparseVector, int, int) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets a sub-vector from a sparse vector.

subVector(Vector, List<Integer>) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).

subVector(Vector, int[]) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).

SubVectorRef - Class in dev.nm.algebra.linear.vector.doubles

Represents a sub-vector backed by the referenced vector, without data copying.

SubVectorRef(Vector, int, int) - Constructor for class dev.nm.algebra.linear.vector.doubles.SubVectorRef

SuccessiveOverrelaxationSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary

The Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method.

SuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SuccessiveOverrelaxationSolver

Construct a SOR solver with the extrapolation factor ω.

sum(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the sum of all elements in the given matrix.

sum(Vector) - Static method in class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

Computes the sum of all vector elements.

sum(int, int) - Method in class dev.nm.analysis.sequence.Summation

Sum up the terms from from to to with the increment 1.

sum(int, int, int) - Method in class dev.nm.analysis.sequence.Summation

Sum up the terms from from to to with the increment inc.

sum(double, double, double) - Method in class dev.nm.analysis.sequence.Summation

Sum up the terms from from to to with the increment inc.

sum(double[]) - Method in class dev.nm.analysis.sequence.Summation

Partial summation of the selected terms.

sum(BigDecimal...) - Static method in class dev.nm.number.big.BigDecimalUtils

Sum up the BigDecimal numbers.

sum(double...) - Static method in class dev.nm.number.big.BigDecimalUtils

Sum up big numbers.

sum(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the sum of the values.

sum(int...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the sum of the values.

sum2(double...) - Static method in class dev.nm.number.doublearray.DoubleArrayMath

Get the sum of squares of the values.

sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_BtDt

Get Σ(Bt)*(dt).

sum_BtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt

Get Σ(Bt)*(dt).

sum_tBtDt() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.F_Sum_tBtDt

Get Σ(t-0.5)*(Bt)*(dt).

Summation - Class in dev.nm.analysis.sequence

Summation is the operation of adding a sequence of numbers; the result is their sum or total.

Summation(Summation.Term, double) - Constructor for class dev.nm.analysis.sequence.Summation

Construct a summation series.

Summation(Summation.Term) - Constructor for class dev.nm.analysis.sequence.Summation

Construct a finite summation series.

Summation.Term - Interface in dev.nm.analysis.sequence

Define the terms in a summation series.

SumOfPenalties - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod

This penalty function sums up the costs from a set of constituent penalty functions.

SumOfPenalties(PenaltyFunction...) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.SumOfPenalties

Construct a sum-of-penalties penalty function from a set of penalty functions.

SumOfPoweredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification

Defines portfolio diversification as \[ D(w) = \sum_i w_i^P \]

SumOfPoweredWeights(double) - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights

SumOfSquaredWeights - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification

Defines portfolio diversification as \[ D(w) = \sum_i w_i^2 \]

SumOfSquaredWeights() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights

sumOfWeights() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean

SumOfWLogW - Class in tech.nmfin.portfoliooptimization.corvalan2005.diversification

Defines portfolio diversification as \[ D(w) = \sum_i w_i \ln(w_i) \]

SumOfWLogW() - Constructor for class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW

sumsOfPowersOfDifferences(int, double, double...) - Static method in class dev.nm.stat.descriptive.moment.Moments

Compute the power-th moment of an array of data with respect to a mean.

sumToInfinity(int) - Method in class dev.nm.analysis.sequence.Summation

Sum up the terms from from to infinity with increment 1 until the series converges.

sumToInfinity(double, double) - Method in class dev.nm.analysis.sequence.Summation

Sum up the terms from from to infinity with increment inc until the series converges.

sumUpLastRows(Matrix, int, int) - Static method in class tech.nmfin.infantino2010.Infantino2010PCA

Sums up, for each column, the last nRows rows.

superDiagonal(Matrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets the super-diagonal of a matrix as a vector.

superDiagonal(SparseMatrix) - Static method in class dev.nm.algebra.linear.vector.doubles.operation.VectorFactory

Gets the super-diagonal of a sparse matrix as a sparse vector.

supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.ChebyshevRule

supportsInterval(double, double) - Method in interface dev.nm.analysis.integration.univariate.riemann.gaussian.rule.GaussianQuadratureRule

Return whether the given interval (a,b) is supported by this rule.

supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.HermiteRule

supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LaguerreRule

supportsInterval(double, double) - Method in class dev.nm.analysis.integration.univariate.riemann.gaussian.rule.LegendreRule

SVD - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd

SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.

SVD(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

Runs the SVD decomposition on a matrix.

SVD(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

Runs the SVD decomposition on a matrix.

SVD(Matrix, boolean, double, SVD.Method) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

Runs the SVD decomposition on a matrix.

svd() - Method in class dev.nm.stat.factor.pca.PCAbySVD

Gets the Singular Value Decomposition (SVD) of matrix X.

SVD.Method - Enum in dev.nm.algebra.linear.matrix.doubles.factorization.svd

SVDbyMR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3

Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR).

SVDbyMR3(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3

Creates a singular value decomposition for a matrix A.

SVDDecomposition - Interface in dev.nm.algebra.linear.matrix.doubles.factorization.svd

SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.

SVEC - Class in dev.nm.algebra.linear.matrix.doubles.operation

SVEC converts a symmetric matrix K = {K_ij} into a vector of dimension n(n+1)/2.

SVEC(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SVEC

Construct the SVEC of a matrix.

svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CSDPMinimizer.Solution

svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.HomogeneousPathFollowingMinimizer.Solution

Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".

svecA() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.PrimalDualPathFollowingMinimizer.Solution

swap(FlexibleTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange

Constructs a new table by exchanging the r-th row with the s-th column in A using Jordan Exchange.

swap(MatrixTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange

Constructs a new table by exchanging the r-th row with the s-th column in A using Jordan Exchange.

swap(int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

Perform a Jordan Exchange to swap row r with column s.

swapColumn(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Swaps two columns of a permutation matrix.

swapColumn(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Swap columns:

swapInPlace(FlexibleTable, int, int) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.JordanExchange

swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

Swaps two rows of a permutation matrix.

swapRow(int, int) - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Swap rows:

symbol1 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

symbol1() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

symbol2 - Variable in class tech.nmfin.meanreversion.cointegration.TradingPair

symbol2() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

SymmetricEigenByMR3 - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR).

SymmetricEigenByMR3(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3

Creates an instance for computing the eigen decomposition for a given symmetric matrix A.

SymmetricEigenByMR3(Matrix, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenByMR3

Creates an instance for computing the eigen decomposition for a given symmetric matrix A.

SymmetricEigenFor2x2Matrix - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3

Computes the eigen decomposition of a 2-by-2 symmetric matrix in the following form by symmetric QR algorithm.

SymmetricEigenFor2x2Matrix(double, double, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.SymmetricEigenFor2x2Matrix

SymmetricKronecker - Class in dev.nm.algebra.linear.matrix.doubles.operation

Compute the symmetric Kronecker product of two matrices.

SymmetricKronecker(Matrix, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.SymmetricKronecker

Compute the symmetric Kronecker product of two matrices.

SymmetricMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle

A symmetric matrix is a square matrix such that its transpose equals to itself, i.e., A[i][j] = A[j][i]

SymmetricMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

Construct a symmetric matrix of dimension dim * dim.

SymmetricMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

Construct a symmetric matrix from a 2D double[][] array.

SymmetricMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

Cast an (almost) symmetric matrix into SymmetricMatrix by averaging A(i,j) and A(j,i).

SymmetricMatrix(Matrix, boolean) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

Cast an (almost) symmetric matrix into SymmetricMatrix.

SymmetricMatrix(SymmetricMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

Copy constructor.

SymmetricQRAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

The symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix.

SymmetricQRAlgorithm(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm

Runs the QR algorithm on a symmetric matrix.

SymmetricQRAlgorithm(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm

Runs the QR algorithm on a symmetric matrix.

SymmetricQRAlgorithm(Matrix, double, int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.SymmetricQRAlgorithm

Runs the QR algorithm on a symmetric matrix.

SymmetricSuccessiveOverrelaxationSolver - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary

The Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in each iteration one forward sweep followed by one backward sweep.

SymmetricSuccessiveOverrelaxationSolver(double, int, Tolerance) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SymmetricSuccessiveOverrelaxationSolver

Construct a SSOR solver with the extrapolation factor ω.

SymmetricSVD - Class in dev.nm.algebra.linear.matrix.doubles.factorization.svd

This algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm.

SymmetricSVD(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD

Calculates the SVD of A.

SymmetricSVD(Matrix, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD

Calculates the SVD of A.

SymmetricTridiagonalDecomposition - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization

Given a square, symmetric matrix A, we find Q such that Q' * A * Q = T , where T is a tridiagonal matrix.

SymmetricTridiagonalDecomposition(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition

Runs the tridiagonal decomposition for a square, symmetric matrix.

SYNC_RNORM - Static variable in class dev.nm.stat.random.rng.RNGUtils

SYNC_UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils

synchronizedRLG(RandomLongGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils

Returns a synchronized (thread-safe) RandomLongGenerator backed by a specified generator.

synchronizedRNG(RandomNumberGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils

Returns a synchronized (thread-safe) RandomNumberGenerator backed by a specified generator.

synchronizedRVG(RandomVectorGenerator) - Static method in class dev.nm.stat.random.rng.RNGUtils

Returns a synchronized (thread-safe) RandomVectorGenerator backed by a specified generator.

SynchronizedStatistic - Class in dev.nm.stat.descriptive

This is a thread-safe wrapper of Statistic by synchronizing all public methods so that only one thread at a time can access the instance.

T

T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.SymmetricTridiagonalDecomposition

Gets the symmetric tridiagonal T matrix.

T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization

Gets T, such that T = Q * A * Q.

T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.QRAlgorithm

T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination

Get the transformation matrix, T, such that T * A = U.

T() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination

Get the transformation matrix, T, such that T * A = U.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

T() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Get the transformation matrix, T, such that T * A = U.

t() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

Get the transpose of this matrix.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

The transpose of a diagonal matrix is the same as itself.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

t(A)

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

The transpose of a symmetric matrix is the same as itself.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

The transpose of a permutation matric is the same as its inverse.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

T() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

Get the transformed matrix T.

t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

t() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D

Gets the time period of interest, that is, the range of t, (0 < t < T).

T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D

Get the time period of interest, that is, the range of t, (0 < t < T).

T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation.ConvectionDiffusionEquation1D

Gets the time period of interest, that is, the range of t, (0 < t < T).

T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation.HeatEquation1D

Gets the time period of interest, that is, the range of t, (0 < t < T).

T() - Method in class dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2.HeatEquation2D

Gets the time period of interest, that is, the range of t, (0 < t < T).

t(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D

Gets the value on the time axis at index k.

t(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the value on the time-axis at index k.

t() - Method in class dev.nm.stat.covariance.covarianceselection.CovarianceSelectionProblem

Gets the penalization parameter t for L1 regularization.

t - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

distinct population eigenvalues

t() - Method in class dev.nm.stat.descriptive.rank.Rank

/[ t = \sum(t_i^3 - t_i) \]

t() - Method in class dev.nm.stat.regression.linear.lasso.ConstrainedLASSOProblem

Get the penalization parameter for the constrained form of LASSO.

t() - Method in class dev.nm.stat.regression.linear.LMBeta

Gets the t- or z- value of the regression coefficients β^.

t() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

Get the current time.

T() - Method in class dev.nm.stat.stochasticprocess.timegrid.EvenlySpacedGrid

Get the end time.

t() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt

Get the current time.

T - Class in dev.nm.stat.test.mean

Student's t-test tests for the equality of means, for the one-sample case, against a hypothetical mean, and for two-sample case, of two populations.

T(double[], double) - Constructor for class dev.nm.stat.test.mean.T

Construct a one-sample location test of whether the mean of a normally distributed population has a value specified in a null hypothesis.

T(double[], double[]) - Constructor for class dev.nm.stat.test.mean.T

Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.

T(double[], double[], double) - Constructor for class dev.nm.stat.test.mean.T

Construct Welch's t-test, an adaptation of Student's t-test, for the use with two samples having possibly unequal variances.

T(double[], double[], boolean, double) - Constructor for class dev.nm.stat.test.mean.T

Construct a two sample location test of the null hypothesis that the means of two normally distributed populations are equal.

T() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal

T - Variable in class tech.nmfin.infantino2010.Infantino2010PCA

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential

Get the lower limit of the integral.

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity

ta() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval

ta() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule

Get the lower limit of the integral.

Table - Interface in dev.nm.misc.datastructure

A table is a means of arranging data in rows and columns.

table - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

tail() - Method in interface dev.nm.graph.Arc

Get the tail of this arc.

tail() - Method in class dev.nm.graph.type.SimpleArc

tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.GramSchmidt

tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.HouseholderQR

tallR() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.qr.QR

tallR() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.qr.QRDecomposition

Get the tall R matrix.

tan(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Tangent of a complex number.

tangentAt(SortedOrderedPairs, int) - Method in interface dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangent

Compute the tangent at the given index k, from the given collection of points.

tanh(Complex) - Static method in class dev.nm.number.complex.ElementaryFunction

Hyperbolic tangent of a complex number.

tau() - Method in class dev.nm.stat.covariance.nlshrink.LedoitWolf2016.Result

Gets estimated population eigenvalues in ascending order.

tau - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

population eigenvalues in ascending order

TauEstimator - Class in dev.nm.stat.covariance.nlshrink

Non-linear shrinkage estimator of population eigenvalues.

TauEstimator(int, int, Vector, int) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator

TauEstimator(int, int, Vector) - Constructor for class dev.nm.stat.covariance.nlshrink.TauEstimator

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential

Get the upper limit of the integral.

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity

tb() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval

tb() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule

Get the upper limit of the integral.

TDistribution - Class in dev.nm.stat.distribution.univariate

The Student t distribution is the probability distribution of t, where \[ t = \frac{\bar{x} - \mu}{s / \sqrt N} \] \(\bar{x}\) is the sample mean; μ is the population mean; s is the square root of the sample variance; N is the sample size; The importance of the Student's distribution is when (as in nearly all practical statistical work) the population standard deviation is unknown and has to be estimated from the data.

TDistribution(double) - Constructor for class dev.nm.stat.distribution.univariate.TDistribution

Construct a Student's t distribution.

tech.nmfin.infantino2010 - package tech.nmfin.infantino2010

tech.nmfin.meanreversion.cointegration - package tech.nmfin.meanreversion.cointegration

tech.nmfin.meanreversion.cointegration.check - package tech.nmfin.meanreversion.cointegration.check

tech.nmfin.meanreversion.daspremont2008 - package tech.nmfin.meanreversion.daspremont2008

tech.nmfin.meanreversion.elliott2005 - package tech.nmfin.meanreversion.elliott2005

tech.nmfin.meanreversion.hvolatility - package tech.nmfin.meanreversion.hvolatility

tech.nmfin.meanreversion.volarb - package tech.nmfin.meanreversion.volarb

tech.nmfin.portfoliooptimization - package tech.nmfin.portfoliooptimization

tech.nmfin.portfoliooptimization.clm - package tech.nmfin.portfoliooptimization.clm

tech.nmfin.portfoliooptimization.corvalan2005 - package tech.nmfin.portfoliooptimization.corvalan2005

tech.nmfin.portfoliooptimization.corvalan2005.constraint - package tech.nmfin.portfoliooptimization.corvalan2005.constraint

tech.nmfin.portfoliooptimization.corvalan2005.diversification - package tech.nmfin.portfoliooptimization.corvalan2005.diversification

tech.nmfin.portfoliooptimization.lai2010 - package tech.nmfin.portfoliooptimization.lai2010

tech.nmfin.portfoliooptimization.lai2010.ceta - package tech.nmfin.portfoliooptimization.lai2010.ceta

tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer - package tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer

tech.nmfin.portfoliooptimization.lai2010.ceta.npeb - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb

tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler - package tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler

tech.nmfin.portfoliooptimization.lai2010.fit - package tech.nmfin.portfoliooptimization.lai2010.fit

tech.nmfin.portfoliooptimization.lai2010.optimizer - package tech.nmfin.portfoliooptimization.lai2010.optimizer

tech.nmfin.portfoliooptimization.markowitz - package tech.nmfin.portfoliooptimization.markowitz

tech.nmfin.portfoliooptimization.markowitz.constraints - package tech.nmfin.portfoliooptimization.markowitz.constraints

tech.nmfin.portfoliooptimization.nmsaam - package tech.nmfin.portfoliooptimization.nmsaam

tech.nmfin.portfoliooptimization.socp.constraints - package tech.nmfin.portfoliooptimization.socp.constraints

tech.nmfin.portfoliooptimization.socp.constraints.ybar - package tech.nmfin.portfoliooptimization.socp.constraints.ybar

tech.nmfin.returns - package tech.nmfin.returns

tech.nmfin.returns.moments - package tech.nmfin.returns.moments

tech.nmfin.trend.dai2011 - package tech.nmfin.trend.dai2011

tech.nmfin.trend.kst1995 - package tech.nmfin.trend.kst1995

temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.BoltzTemperatureFunction

temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.ExpTemperatureFunction

Matlab's default: @temperatureexp (default) - T = T0 * 0.95^k.

temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.FastTemperatureFunction

Matlab: @temperaturefast - The temperature is equal to InitialTemperature / k.

temperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction

Gets the temperature at time t.

TemperatureFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction

A temperature function defines a temperature schedule used in simulated annealing.

TemperedAcceptanceProbabilityFunction - Interface in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction

A tempered acceptance probability function computes the probability that the next state transition will be accepted.

test(double) - Method in interface dev.nm.number.DoubleUtils.ifelse

Decide whether x satisfies the boolean test.

theta - Variable in class dev.nm.stat.hmm.mixture.distribution.GammaMixtureDistribution.Lambda

the scale parameter

theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial

theta(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution

The canonical parameter of the distribution in terms of the mean μ.

theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma

theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian

theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian

theta(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson

theta() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OrnsteinUhlenbeckProcess

theta() - Method in interface dev.nm.stat.stochasticprocess.univariate.sde.process.ou.OUProcess

Get the mean reversion rate.

theta() - Method in class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Get all the MA coefficients.

theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep

Get the coefficients of the linear predictor.

theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep

Get the coefficients of the linear predictor.

theta(int, int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateInnovationAlgorithm

Get the coefficients of the linear predictor.

theta() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get all the MA coefficients.

theta(int, int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm

Gets the coefficients of the linear predictor.

thetaPolynomial() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

Get the polynomial (1 + θ).

ThinRNG - Class in dev.nm.stat.random.rng.univariate

Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.

ThinRNG(RandomNumberGenerator, int) - Constructor for class dev.nm.stat.random.rng.univariate.ThinRNG

Constructs a thinned RNG.

ThinRVG - Class in dev.nm.stat.random.rng.multivariate

Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw.

ThinRVG(RandomVectorGenerator, int) - Constructor for class dev.nm.stat.random.rng.multivariate.ThinRVG

Constructs a thinned RVG.

ThomasAlgorithm - Class in dev.nm.algebra.linear.matrix.doubles.linearsystem

Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.

ThomasAlgorithm() - Constructor for class dev.nm.algebra.linear.matrix.doubles.linearsystem.ThomasAlgorithm

ThreadIDRLG - Class in dev.nm.stat.random.rng.concurrent.context

This uniform number generator generates independent sequences of random numbers per thread, hence thread-safe.

ThreadIDRLG(int, long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG

Constructs a per-context repeatable RLG.

ThreadIDRLG(int) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG

Constructs a per-context repeatable RLG.

ThreadIDRLG() - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG

Constructs a per-context repeatable RLG.

ThreadIDRLG(long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRLG

Constructs a per-context repeatable RLG.

ThreadIDRNG - Class in dev.nm.stat.random.rng.concurrent.context

This random number generator generates independent sequences of random numbers per thread, hence thread-safe.

ThreadIDRNG(int, long) - Constructor for class dev.nm.stat.random.rng.concurrent.context.ThreadIDRNG

Constructs a per-context repeatable RNG.

threshold(double) - Method in class tech.nmfin.meanreversion.elliott2005.Elliott2005DLM

Gets a suggested trading threshold based on an OU process.

throwIfDifferentDimension(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Throws if A1.nRows() != A2.nRows() Or A1.nCols() != A2.nCols()

throwIfIncompatible4Multiplication(Table, Table) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Throws if A1.nCols() != A2.nRows()

throwIfIncompatible4Multiplication(Table, Vector) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Throws if A.nCols() != v.size()

throwIfInvalidColumn(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Throws if accessing an out of range column.

throwIfInvalidIndex(Vector, int) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if an index is a valid index.

throwIfInvalidRow(Table, int) - Static method in class dev.nm.misc.datastructure.DimensionCheck

Throws if accessing an out of range row.

throwIfNotEqualSize(Vector, Vector) - Static method in class dev.nm.algebra.linear.matrix.doubles.MatrixPropertyUtils

Checks if the input vectors have the same size.

throwIfNotNull(RuntimeException) - Static method in class dev.nm.misc.ExceptionUtils

This is a wrapper method that throws a RuntimeException if it is not null.

Ties<T> - Class in dev.nm.combinatorics

Count the number of occurrences of each distinctive value.

Ties(List<T>) - Constructor for class dev.nm.combinatorics.Ties

Count the number of occurrences of each distinctive value.

time() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row

Gets the time of the row.

time() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

Get the current time.

time(int) - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration

Get the i-th time point.

time() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess

Get the current time.

time(int) - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Get the i-th timestamp.

time(int) - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

Get the i-th time.

time() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast

Gets the forecast time.

TimeGrid - Interface in dev.nm.stat.stochasticprocess.timegrid

Specify the time points in a grid or axis.

TimeInterval - Class in dev.nm.misc.datastructure.time

This is a time interval.

TimeInterval(DateTime, DateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeInterval

Construct a time interval from two time points.

TimeIntervals - Class in dev.nm.misc.datastructure.time

This is a collection of time intervals TimeInterval.

TimeIntervals() - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals

Construct an empty collection of time interval.

TimeIntervals(DateTime, DateTime) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals

Construct a collection consisting of one time interval.

TimeIntervals(Interval<DateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals

Construct a collection consisting of one time interval.

TimeIntervals(Interval<DateTime>...) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals

Construct a collection of time intervals.

TimeIntervals(Intervals<DateTime>) - Constructor for class dev.nm.misc.datastructure.time.TimeIntervals

Copy constructor.

times() - Method in class dev.nm.stat.stochasticprocess.univariate.filtration.Filtration

Get the entire time grid.

times() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

TimeSeries<T extends Comparable<? super T>,V,E extends TimeSeries.Entry<T,V>> - Interface in dev.nm.stat.timeseries.datastructure

A time series is a serially indexed collection of items.

TimeSeries.Entry<T,V> - Interface in dev.nm.stat.timeseries.datastructure

A time series is composed of a sequence of Entrys.

timestamps() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Get all the timestamps.

timestamps() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

Get all the timestamps.

to1DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get all matrix entries in the form of an 1D double[].

to2DArray(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get all matrix entries in the form of a 2D double[][] array.

toArray() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

toArray() - Method in class dev.nm.algebra.linear.vector.doubles.CombinedVectorByRef

toArray() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

toArray() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

toArray() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

toArray() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Cast this vector into a 1D double[].

toArray() - Method in class dev.nm.misc.datastructure.IdentityHashSet

toArray(T[]) - Method in class dev.nm.misc.datastructure.IdentityHashSet

toArray() - Method in class dev.nm.misc.datastructure.MathTable.Row

Converts the row to a double[], excluding the index.

toArray() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

Get the sorted sample.

toArray() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

Convert this multivariate time series into an array of vectors.

toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.DifferencedIntTimeTimeSeries

toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

toArray() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.OneDimensionTimeSeries

toArray() - Method in interface dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeries

Convert this time series into an array, discarding the timestamps.

toBigDecimal() - Method in class dev.nm.number.Real

Convert this number to a BigDecimal.

toColumns(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get an array of all column vectors from a matrix.

toContext(HouseholderInPlace.Householder) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace.Householder

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

toDense() - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.Densifiable

Densify a matrix, i.e., convert a matrix implementation to the standard dense matrix, DenseMatrix.

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

toDense() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

toDouble() - Method in class dev.nm.number.complex.Complex

Cast the complex number to a Double if it is a real number.

toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralLessThanConstraints

toGreaterThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.LessThanConstraints

Convert the less-than or equal-to constraints to greater-than or equal-to constraints.

toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

toGreaterThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearLessThanConstraints

Tolerance - Interface in dev.nm.misc.algorithm.iterative.tolerance

The tolerance criteria for an iterative algorithm to stop.

toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.general.GeneralGreaterThanConstraints

toLessThanConstraints() - Method in interface dev.nm.solver.multivariate.constrained.constraint.GreaterThanConstraints

Convert the greater-than or equal-to constraints to less-than or equal-to constraints.

toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

toLessThanConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearGreaterThanConstraints

toMatrix() - Method in class dev.nm.misc.datastructure.FlexibleTable

Gets a copy of the flexible table in the form of a matrix.

toMatrix() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

toMatrix() - Method in interface dev.nm.stat.timeseries.datastructure.multivariate.MultivariateTimeSeries

Convert this multivariate time series into an m x n matrix, where m is the dimension, and n the length.

toMatrix() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

toMatrix(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils

Cast a time series into a column matrix, discarding the timestamps.

TopNOptimizationAlgorithm - Class in tech.nmfin.portfoliooptimization

TopNOptimizationAlgorithm(PortfolioOptimizationAlgorithm, int, double) - Constructor for class tech.nmfin.portfoliooptimization.TopNOptimizationAlgorithm

topologicalOrder(V) - Method in interface dev.nm.graph.DAGraph

Get the topological order of a vertex.

topologicalOrder(V) - Method in class dev.nm.graph.type.SparseDAGraph

topologicalOrder(V) - Method in class dev.nm.graph.type.SparseTree

topologicalOrder(VertexTree<T>) - Method in class dev.nm.graph.type.VertexTree

toPrimitive(Double[]) - Static method in class dev.nm.number.DoubleUtils

Convert a Double array to a primitive double array.

toRows(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get an array of all row vectors from a matrix.

toStop(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.eigen.InverseIteration.StoppingCriterion

Check whether we stop with the current eigenvector.

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.CharacteristicPolynomial

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.DefaultMatrixStorage

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.MatrixCoordinate

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry

toString(SparseMatrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrixUtils

Returns a string representation of a SparseMatrix.

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ElementaryOperation

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext

toString(MatrixTable) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixUtils

Get the String representation of a matrix.

toString() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

toString() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

toString() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

toString() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

toString() - Method in class dev.nm.algebra.linear.vector.doubles.SubVectorRef

toString() - Method in class dev.nm.analysis.function.polynomial.Polynomial

toString() - Method in class dev.nm.analysis.function.rn2r1.QuadraticFunction

toString() - Method in class dev.nm.analysis.function.tuple.Triple

toString() - Method in class dev.nm.graph.algorithm.traversal.BFS.Node

toString() - Method in class dev.nm.graph.algorithm.traversal.DFS.Node

toString() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

toString() - Method in class dev.nm.graph.community.EdgeBetweeness

toString() - Method in class dev.nm.graph.community.GirvanNewman

toString() - Method in class dev.nm.graph.type.SimpleArc

toString() - Method in class dev.nm.graph.type.SimpleEdge

toString() - Method in class dev.nm.graph.type.SparseDiGraph

toString() - Method in class dev.nm.graph.type.SparseGraph

toString() - Method in class dev.nm.graph.type.VertexTree

toString() - Method in class dev.nm.interval.Interval

toString() - Method in class dev.nm.interval.Intervals

toString() - Method in class dev.nm.misc.algorithm.ActiveSet

toString() - Method in class dev.nm.misc.datastructure.FlexibleTable

toString() - Method in class dev.nm.misc.datastructure.IdentityHashSet

toString() - Method in class dev.nm.misc.datastructure.SortableArray

toString() - Method in class dev.nm.number.complex.Complex

toString(double...) - Static method in class dev.nm.number.DoubleUtils

Print out numbers to a string.

toString(double[][]) - Static method in class dev.nm.number.DoubleUtils

Print out a 2D array, double[][] to a string.

toString() - Method in class dev.nm.number.Real

toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound

toString() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.LinearConstraints

toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable

toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem.LPProblemImpl1

toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label

toString() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPBoundedMinimizer

toString() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode

toString() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome

toString() - Method in class dev.nm.stat.descriptive.covariance.Covariance

toString() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

toString() - Method in class dev.nm.stat.descriptive.moment.Mean

toString() - Method in class dev.nm.stat.descriptive.moment.Moments

toString() - Method in class dev.nm.stat.descriptive.moment.Skewness

toString() - Method in class dev.nm.stat.descriptive.moment.Variance

toString() - Method in class dev.nm.stat.descriptive.rank.Max

toString() - Method in class dev.nm.stat.descriptive.rank.Min

toString() - Method in class dev.nm.stat.evt.cluster.Clusters.Cluster

toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERConfidenceInterval

toString() - Method in class dev.nm.stat.evt.evd.univariate.fitting.acer.ACERFunction.ACERParameter

toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row

toString() - Method in class dev.nm.stat.regression.linear.panel.PanelData

toString() - Method in class dev.nm.stat.test.distribution.pearson.AS159.RandomMatrix

toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.MultivariateGenericTimeTimeSeries

toString() - Method in class dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime.MultivariateSimpleTimeSeries

toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.GenericTimeTimeSeries

toString() - Method in class dev.nm.stat.timeseries.datastructure.univariate.realtime.inttime.SimpleTimeSeries

toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast

toString() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAXModel

toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares

toString() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

toString() - Method in class tech.nmfin.meanreversion.cointegration.TradingPair

toString() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

toString() - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift

toString() - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged

toString() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution

toString() - Method in class tech.nmfin.trend.dai2011.Dai2011HMM

toString() - Method in class tech.nmfin.trend.kst1995.KnightSatchellTran1995

toVector(UnivariateTimeSeries<?, ?>) - Static method in class dev.nm.stat.timeseries.datastructure.univariate.UnivariateTimeSeriesUtils

Cast a time series into a vector, discarding the timestamps.

tr(Matrix) - Static method in class dev.nm.algebra.linear.matrix.doubles.operation.MatrixMeasure

Compute the sum of the diagonal elements, i.e., the trace of a matrix.

track(V, int) - Method in class dev.nm.graph.algorithm.traversal.BFS

track(V, int) - Method in class dev.nm.graph.algorithm.traversal.DFS

track(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots

Runs the traversal algorithm on a graph from a designated root.

trades() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

TradingPair - Class in tech.nmfin.meanreversion.cointegration

TradingPair(String, String, Vector, Vector, double) - Constructor for class tech.nmfin.meanreversion.cointegration.TradingPair

Constructs a related pair for trading, e.g., cointegrated pair.

train(int[], DiscreteHMM) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch

Constructs a trained (discrete) hidden Markov model, one iteration.

train(MixtureHMM, double[]) - Static method in class dev.nm.stat.hmm.mixture.MixtureHMMEM

Constructs a trained mixture hidden Markov model, one iteration.

transform(double[]) - Method in interface dev.nm.dsp.univariate.operation.system.doubles.Filter

Transforms the input signal into the output signal.

transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverage

transform(double[]) - Method in class dev.nm.dsp.univariate.operation.system.doubles.MovingAverageByExtension

transform(QPProblem) - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.socp.QPtoSOCPTransformer

transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.AutoParallelMatrixMathOperation

transpose(MatrixAccess) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation

Get the transpose of A.

transpose(MatrixAccess) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation

transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner

Return the input vector x.

transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.JacobiPreconditioner

P^t = P^-1 for Jacobi preconditioner.

transposeSolve(Vector) - Method in interface dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.Preconditioner

Solve M^tv = x, where M is the preconditioner matrix.

transposeSolve(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.SSORPreconditioner

M^tx = M^-1x as M is symmetric.

Trapezoidal - Class in dev.nm.analysis.integration.univariate.riemann.newtoncotes

The Trapezoidal rule is a closed type Newton-Cotes formula, where the integral interval is evenly divided into N sub-intervals.

Trapezoidal(double, int) - Constructor for class dev.nm.analysis.integration.univariate.riemann.newtoncotes.Trapezoidal

Construct an integrator that implements the Trapezoidal rule.

TraversalFromRoots<V> - Class in dev.nm.graph.algorithm.traversal

A graph traversal is the problem of visiting all the nodes in a graph in a particular manner.

TraversalFromRoots(Graph<? extends V, ? extends Edge<V>>) - Constructor for class dev.nm.graph.algorithm.traversal.TraversalFromRoots

Constructs a traversal order of a graph.

traverse(V, int) - Method in class dev.nm.graph.algorithm.traversal.TraversalFromRoots

Runs the traversal algorithm on a graph from a designated root.

Tree<V,E extends HyperEdge<V>> - Interface in dev.nm.graph

A tree is an undirected graph in which any two vertices are connected by exactly one simple path.

trees() - Method in interface dev.nm.graph.Forest

Get the disjoint set of trees.

trend() - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

TrendType - Enum in dev.nm.stat.test.timeseries.adf

These are the three versions of the Augmented Dickey-Fuller (ADF) test.

TriangularDistribution - Class in dev.nm.stat.distribution.univariate

The triangular distribution is a continuous probability distribution with lower limit a, upper limit b and mode c, where a < b and a ≤ c ≤ b.

TriangularDistribution(double, double, double) - Constructor for class dev.nm.stat.distribution.univariate.TriangularDistribution

Constructs an instance of a Triangular distribution.

TridiagonalDeflationSearch - Class in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr

This class locates deflation in a tridiagonal matrix.

TridiagonalDeflationSearch(boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch

TridiagonalDeflationSearch(DeflationCriterion, boolean, double) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr.TridiagonalDeflationSearch

TriDiagonalization - Class in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization

A tri-diagonal matrix A is a matrix such that it has non-zero elements only in the main diagonal, the first diagonal below, and the first diagonal above.

TriDiagonalization(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.TriDiagonalization

Runs the tri-diagonalization process for a symmetric matrix.

TridiagonalMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal

A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.

TridiagonalMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

Constructs a 0 tri-diagonal matrix of dimension dim * dim.

TridiagonalMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

Constructs a tri-diagonal matrix from a 3-row 2D double[][] array such that: the first row is the super diagonal with (dim - 1) entries; the second row is the main diagonal with dim entries; the third row is the sub diagonal with (dim - 1) entries. For example,

TridiagonalMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

Casts a matrix to tridiagonal by copying the 3 diagonals (ignoring all other entries).

TridiagonalMatrix(TridiagonalMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

Copy constructor performing a deep copy.

Trigamma - Class in dev.nm.analysis.function.special.gamma

The trigamma function is defined as the logarithmic derivative of the digamma function.

Trigamma() - Constructor for class dev.nm.analysis.function.special.gamma.Trigamma

TrigMath - Class in dev.nm.geometry

A collection of trigonometric functions complementary to those in Java's Math class.

Triple - Class in dev.nm.analysis.function.tuple

A triple is a tuple of length three.

Triple(double, double, double) - Constructor for class dev.nm.analysis.function.tuple.Triple

Creates a new triple with the given values.

TrivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1

A trivariate real function takes three real arguments and outputs one real value.

TruncatedNormalDistribution - Class in dev.nm.stat.distribution.univariate

The truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both).

TruncatedNormalDistribution(double, double, double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

Construct a truncated Normal distribution.

TruncatedNormalDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

Construct a truncated standard Normal distribution.

TSS() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the diagnostic measure: total sum of squares, \(\sum (y-y_mean)^2 \).

TurningPoint - Class in tech.nmfin.portfoliooptimization.clm

Represents a turning point on a Markowitz critical line.

Twiddle<T> - Class in dev.nm.combinatorics

Generates all combinations of M elements drawn without replacement from a set of N elements.

Twiddle(Collection<T>, int) - Constructor for class dev.nm.combinatorics.Twiddle

type - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.Label

the label type

U

U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization

Gets U, where U^' = U_k * ...

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder

Gets U, where U^' = U_k * ...

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination

Get the upper triangular matrix, U, such that T * A = U and P * A = L * U.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussianElimination4SquareMatrix

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination

Get the reduced row echelon form matrix, U, such that T * A = U.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition

Get the U matrix as in SVD decomposition.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD

Returns the matrix U as in A=UDV'.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.Doolittle

U() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LU

U() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.triangle.LUDecomposition

Get the upper triangular matrix U as in the LU decomposition.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel

Get the upper triangular matrix U, such that T * A = U.

U() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Gets the accumulated Householder reflections applied to A.

u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D

Gets the value of the grid point at (x_k, y_j).

u(int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D

Gets the value of the grid point at (t_m, x_n).

u(int, int, int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the value of the grid point at (t_k, x_i, y_j).

u - Variable in class dev.nm.analysis.differentialequation.pde.finitedifference.PDETimeSpaceGrid1D

the solution matrix

u() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial

Get u as in (x² + ux + v).

unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Compute the multivariate ARMA unconditional mean.

unconditionalMean() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAModel

Compute the multivariate ARMA unconditional mean.

unconstrainedFactory - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained.ConstrainedCellFactory

a factory that defines the unconstrained Differential Evolution operators

UnconstrainedLASSObyCoordinateDescent - Class in dev.nm.stat.regression.linear.lasso

This class solves the unconstrained form of LASSO, that is, \[ \min_w \left \{ \left \| Xw - y \right \|_2^2 + \lambda * \left \| w \right \|_1 \right \} \] by Coordinate Descent method.

UnconstrainedLASSObyCoordinateDescent(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyCoordinateDescent

Solves an unconstrained LASSO problem by Coordinate Descent method.

UnconstrainedLASSObyQP - Class in dev.nm.stat.regression.linear.lasso

This class solves the unconstrained form of LASSO (i.e.

UnconstrainedLASSObyQP(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSObyQP

Solves an unconstrained LASSO problem by transforming it into a single quadratic programming problem.

UnconstrainedLASSOProblem - Class in dev.nm.stat.regression.linear.lasso

A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization.

UnconstrainedLASSOProblem(Vector, Matrix, double) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem

Constructs a LASSO problem.

UnconstrainedLASSOProblem(UnconstrainedLASSOProblem) - Constructor for class dev.nm.stat.regression.linear.lasso.UnconstrainedLASSOProblem

Copy constructor.

UNDEFINED - Static variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable

unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V, GraphUtils.EdgeFactory<V, N, E, UndirectedEdge<V>>) - Static method in class dev.nm.graph.GraphUtils

Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.

unDi2DAGraph(UnDiGraph<V, ? extends UndirectedEdge<V>>, V) - Static method in class dev.nm.graph.GraphUtils

Converts an undirected graph into a directed acyclic graph, arcs are created from the edges by parent-child relations as determined by breadth-first-search.

UnDiGraph<V,E extends UndirectedEdge<V>> - Interface in dev.nm.graph

An undirected graph is a graph, or set of nodes connected by edges, where an edge does not differentiate between (a, b) or (b, a).

UndirectedEdge<V> - Interface in dev.nm.graph

A tagging interface for implementations of an undirected graph that accept only undirected edges.

uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.GeneticAlgorithm

This is a uniform random number generator.

uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleCellFactory

the uniform random number generator

uniform - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer

UNIFORM - Static variable in class dev.nm.stat.random.rng.RNGUtils

UNIFORM_WEIGHTING - Static variable in class dev.nm.analysis.curvefit.LeastSquares

A uniform weighting.

UniformDistributionOverBox - Class in dev.nm.stat.random.rng.multivariate

This random vector generator uniformly samples points over a box region.

UniformDistributionOverBox(RandomLongGenerator, RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox

Construct a random vector generator to uniformly sample points over a box region.

UniformDistributionOverBox(RealInterval...) - Constructor for class dev.nm.stat.random.rng.multivariate.UniformDistributionOverBox

Construct a random vector generator to uniformly sample points over a box region.

UniformDistributionOverBox1 - Class in dev.nm.solver.multivariate.initialization

This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.

UniformDistributionOverBox1(RandomLongGenerator, int, RealInterval...) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox1

Construct a generator to uniformly sample points over a feasible region.

UniformDistributionOverBox2 - Class in dev.nm.solver.multivariate.initialization

This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region, with some degree of irregularity or randomness.

UniformDistributionOverBox2(double, RealInterval[], int[], RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2

Construct a generator to uniformly sample points over a feasible region.

UniformDistributionOverBox2(double, RealInterval[], int, RandomLongGenerator) - Constructor for class dev.nm.solver.multivariate.initialization.UniformDistributionOverBox2

Construct a generator to uniformly sample points over a feasible region.

UniformMeshOverRegion - Class in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration

The initial population is generated by putting a uniform mesh/grid/net over the entire region.

UniformMeshOverRegion(RealScalarFunction, SimpleCellFactory, RandomLongGenerator, int, Vector[], double) - Constructor for class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration.UniformMeshOverRegion

Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire region.

UniformRNG - Class in dev.nm.stat.random.rng.univariate.uniform

A pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval.

UniformRNG(UniformRNG.Method) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

Construct a pseudo uniform random number generator.

UniformRNG() - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

Construct a pseudo uniform random number generator.

UniformRNG(long...) - Constructor for class dev.nm.stat.random.rng.univariate.uniform.UniformRNG

Construct a seeded pseudo uniform random number generator.

UniformRNG.Method - Enum in dev.nm.stat.random.rng.univariate.uniform

the pseudo uniform random number generators available

Uniroot - Interface in dev.nm.analysis.root.univariate

A root-finding algorithm is a numerical algorithm for finding a value x such that f(x) = 0, for a given function f.

UnitGrid - Class in dev.nm.stat.stochasticprocess.timegrid

This is the sequence of time points [0, 1, ..., T].

UnitGrid(int) - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid

Construct a sequence of time points [0, 1, ..., T].

UnitGrid() - Constructor for class dev.nm.stat.stochasticprocess.timegrid.UnitGrid

Construct a sequence of time points [0, 1, ..., ∞].

unitRoundOff(int, int) - Static method in class dev.nm.misc.Constants

Get the unit round off as defined in the reference.

unitRoundOff() - Static method in class dev.nm.misc.Constants

Get the default unit round off.

UnivariateEVD - Interface in dev.nm.stat.evt.evd.univariate

Distribution of extreme values (e.g., maxima, minima, or other order statistics).

UnivariateMinimizer - Interface in dev.nm.solver.univariate

A univariate minimizer minimizes a univariate function.

UnivariateMinimizer.Solution - Interface in dev.nm.solver.univariate

This is the solution to a univariate minimization problem.

UnivariateRealFunction - Interface in dev.nm.analysis.function.rn2r1.univariate

A univariate real function takes one real argument and outputs one real value.

UnivariateTimeSeries<T extends Comparable<? super T>,E extends UnivariateTimeSeries.Entry<T>> - Interface in dev.nm.stat.timeseries.datastructure.univariate

This is a univariate time series indexed by some notion of time.

UnivariateTimeSeries.Entry<T> - Class in dev.nm.stat.timeseries.datastructure.univariate

This is the TimeSeries.Entry for a univariate time series.

UnivariateTimeSeriesUtils - Class in dev.nm.stat.timeseries.datastructure.univariate

These are the utility functions to manipulate a univariate time series.

UnsatisfiableErrorCriterionException - Exception in dev.nm.analysis.differentialequation

An exception that is thrown when the error criterion cannot be met.

UnsatisfiableErrorCriterionException(String) - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException

UnsatisfiableErrorCriterionException() - Constructor for exception dev.nm.analysis.differentialequation.UnsatisfiableErrorCriterionException

update(double) - Method in class tech.nmfin.meanreversion.hvolatility.KagiModel

update(DateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MeanEstimator

Updates the model with the current price.

update(DateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MeanEstimatorMaxLevelShift

update(DateTime, double) - Method in interface tech.nmfin.meanreversion.volarb.MRModel

Updates the model with the current price.

update(DateTime, double) - Method in class tech.nmfin.meanreversion.volarb.MRModelRanged

updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation

Update the Hessian matrix using the latest iterates.

updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

updateHessian(Vector, Vector, Vector, Vector, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation2

updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in interface dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation

Update the Hessian matrix using the latest iterates.

updateHessian(Vector, Vector, Vector, Vector, Vector, Matrix, Matrix, Matrix) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.SQPASVariation1

Update the Hessian matrix using the latest iterates.

updateHessianInverse(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.DFPMinimizer

S_k+1 = S_k + δδ' / γ'δ - Sγγ'S' / γ'Sγ

updateHessianInverse1(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer

S_k+1 = S_k + (1 + γ'Sγ/γ'δ)/γ'δ * δδ' -(δγ'S + Sγδ') / γ'δ, where S = H^-1

updateHessianInverse2(Matrix, Matrix, Matrix) - Static method in class dev.nm.solver.multivariate.unconstrained.c2.quasinewton.BFGSMinimizer

P + γγ' / γ'δ - P %*% γγ' %*% P / γ'Pδ, where P = S^-1 is the Hessian.

updateStates() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

Update the bracketing interval and the best min found so far.

updateStates() - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

upper() - Method in class dev.nm.interval.RealInterval

Get the upper bound of this interval.

upper() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints.Bound

upperBound() - Method in class dev.nm.solver.multivariate.constrained.problem.BoxOptimProblem

Gets the upper bounds.

upperBoundConstraints() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

Split the box constraints and get the less-than-the-upper-bounds part.

UpperBoundConstraints - Class in dev.nm.solver.multivariate.constrained.constraint.linear

This is an upper bound constraints such that for all x_i's, x_i ≤ b

UpperBoundConstraints(RealScalarFunction, double) - Constructor for class dev.nm.solver.multivariate.constrained.constraint.linear.UpperBoundConstraints

Construct an upper bound constraints for all variables in a function.

upperBounds() - Method in class dev.nm.solver.multivariate.constrained.constraint.linear.BoxConstraints

Gets the upper bounds.

UpperTriangularMatrix - Class in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle

An upper triangular matrix has 0 entries where row index is greater than column index.

UpperTriangularMatrix(int) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

Constructs an upper triangular matrix of dimension dim * dim.

UpperTriangularMatrix(double[][]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

Constructs an upper triangular matrix from a 2D double[][] array.

UpperTriangularMatrix(Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

Constructs an upper triangular matrix from a matrix.

UpperTriangularMatrix(UpperTriangularMatrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

Copy constructor.

Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD

Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3

Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3

Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

Ut() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition

Get the transpose of U, i.e., U().t().

Ut() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD

Returns the matrix U' as in A=UDV'.

V

V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalization

Gets V, where V^' = V_k * ...

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByGolubKahanLanczos

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization.BiDiagonalizationByHouseholder

Gets V, where V^' = V_k * ...

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.GolubKahanSVD

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD

V() - Method in interface dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVDDecomposition

Get the V matrix as in SVD decomposition.

V() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.SymmetricSVD

Returns the matrix V as in A=UDV'.

v - Variable in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderContext

The defining vector which is perpendicular to the Householder hyperplane.

v() - Method in class dev.nm.analysis.function.polynomial.QuadraticMonomial

Get v as in (x² + ux + v).

v() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizer

When the problem is unbounded, the direction of arbitrarily negative can be computed by adjusting λ.

v() - Method in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution.LPUnboundedMinimizerScheme2

V(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Gets V(t), the covariance matrix of v_t.

V(int) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation

Get V(t), the variance of v_t.

V() - Method in class dev.nm.stat.factor.pca.PCAbyEigen

Gets the correlation (or covariance) matrix used by the PCA.

V() - Method in class tech.nmfin.returns.moments.ReturnsMoments

Gets the second moment matrix.

validate(String) - Static method in class dev.nm.misc.license.Package

validateAll(String...) - Static method in class dev.nm.misc.license.Package

validateAny(String...) - Static method in class dev.nm.misc.license.Package

validateVersion(String, String) - Static method in class dev.nm.misc.license.Package

Check if a package is licensed up to a specified version.

value - Variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry

the entry value

value() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry

Gets the value of this entry.

value() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.InnerProduct

Get the value of the inner product.

value() - Method in class dev.nm.analysis.integration.univariate.Lebesgue

Get the integral value.

value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.EdgeBetweeness

Gets the edge-betweeness of an edge.

value(UndirectedEdge<V>) - Method in class dev.nm.graph.community.GirvanNewman

Get the edge-betweeness of an edge.

value() - Method in interface dev.nm.misc.algorithm.bb.BBNode

the value of this node

value() - Method in class dev.nm.solver.multivariate.constrained.integer.linear.bb.ILPNode

value() - Method in class dev.nm.stat.descriptive.correlation.KendallRankCorrelation

value() - Method in class dev.nm.stat.descriptive.correlation.SpearmanRankCorrelation

value() - Method in class dev.nm.stat.descriptive.covariance.Covariance

value() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

value() - Method in class dev.nm.stat.descriptive.moment.Mean

value() - Method in class dev.nm.stat.descriptive.moment.Moments

value() - Method in class dev.nm.stat.descriptive.moment.Skewness

value() - Method in class dev.nm.stat.descriptive.moment.Variance

value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedMean

value() - Method in class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

value() - Method in class dev.nm.stat.descriptive.rank.Max

value() - Method in class dev.nm.stat.descriptive.rank.Min

value(double) - Method in class dev.nm.stat.descriptive.rank.Quantile

Compute the sample value corresponding to a quantile.

value() - Method in class dev.nm.stat.descriptive.rank.Quantile

value() - Method in interface dev.nm.stat.descriptive.Statistic

Get the value of the statistic.

value() - Method in class dev.nm.stat.descriptive.SynchronizedStatistic

value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByClusterSizeReciprocal

value(double[]) - Method in class dev.nm.stat.evt.exi.ExtremalIndexByFerroSeegers

value(double[]) - Method in interface dev.nm.stat.evt.exi.ExtremalIndexEstimation

Estimate the extremal index with the given observations from a distribution.

value() - Method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent

Gets the value of this Mersenne exponent.

value() - Method in class dev.nm.stat.random.sampler.resampler.BootstrapEstimator

Gets the estimator value (the mean).

value(double[], double[], double[]) - Method in class dev.nm.stat.regression.WeightedRSS

Computes the weighted RSS for a set of observations.

value(Filtration) - Method in class dev.nm.stat.stochasticprocess.univariate.integration.Integral

Integrate the function with respect to a given filtration.

value() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime

Get the expectation (the mean).

value(double[]) - Method in interface tech.nmfin.portfoliooptimization.corvalan2005.diversification.DiversificationMeasure

Evaluates the level of portfolio diversification given portfolio weights.

value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.ProductOfWeights

value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfPoweredWeights

value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfSquaredWeights

value(double[]) - Method in class tech.nmfin.portfoliooptimization.corvalan2005.diversification.SumOfWLogW

value() - Method in class tech.nmfin.portfoliooptimization.lai2010.ceta.maximizer.CetaMaximizer.Solution

ValueArray(int[], int[], double[]) - Constructor for class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.Dfdx.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.differentiation.univariate.FiniteDifference.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.function.special.gamma.LogGamma.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.graph.algorithm.traversal.DFS.Node.Color

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.interval.IntervalRelation

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Quantile.QuantileType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.descriptive.rank.Rank.TiesMethod

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type

Deprecated.

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.timeseries.adf.TrendType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.test.variance.Levene.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum tech.nmfin.infantino2010.Infantino2010Regime.Regime

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum tech.nmfin.meanreversion.hvolatility.Kagi.Trend

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum tech.nmfin.returns.ReturnsCalculators

Returns the enum constant of this type with the specified name.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.eigen.Eigen.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.factorization.svd.SVD.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.linearsystem.Kernel.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix.BidiagonalMatrixType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.ConvergenceFailure.Reason

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.Entry.TopLeftFirstComparator

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseMatrix.ValueArray

values() - Static method in enum dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector.Entry.Comparator

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.curvefit.interpolation.univariate.CubicHermite.Tangents

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.differentiation.univariate.Dfdx.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.differentiation.univariate.FiniteDifference.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.function.special.gamma.LogGamma.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.integration.univariate.riemann.newtoncotes.NewtonCotes.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity.PowerLawSingularityType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Method in class dev.nm.combinatorics.Ties

Get the numbers of occurrences of the objects.

values() - Static method in enum dev.nm.dsp.univariate.operation.system.doubles.MovingAverage.Side

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.graph.algorithm.traversal.DFS.Node.Color

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.interval.IntervalRelation

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.number.DoubleUtils.RoundingScheme

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.SimplexTable.LabelType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.solver.multivariate.unconstrained.c2.steepestdescent.FirstOrderMinimizer.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.Test

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.cointegration.JohansenAsymptoticDistribution.TrendType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.descriptive.rank.Quantile.QuantileType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.descriptive.rank.Rank.TiesMethod

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.evt.markovchain.ExtremeValueMC.MarginalDistributionType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.factor.factoranalysis.FactorAnalysis.ScoringRule

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.random.rng.univariate.uniform.mersennetwister.dynamiccreation.MersenneExponent

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.random.rng.univariate.uniform.UniformRNG.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.random.sampler.resampler.bootstrap.block.PattonPolitisWhite2009ForObject.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Method in class dev.nm.stat.regression.linear.panel.PanelData.Row

Gets the values of the row.

values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Side

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovSmirnov.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution.Side

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.distribution.pearson.ChiSquareIndependenceTest.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.timeseries.adf.ADFAsymptoticDistribution1.Type

Deprecated.

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.timeseries.adf.TrendType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.test.variance.Levene.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCovariance.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHFit.GRADIENT

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum tech.nmfin.infantino2010.Infantino2010Regime.Regime

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum tech.nmfin.meanreversion.hvolatility.Kagi.Trend

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum tech.nmfin.returns.ReturnsCalculators

Returns an array containing the constants of this enum type, in the order they are declared.

VanDerWaerden - Class in dev.nm.stat.test.rank

The Van der Waerden test tests for the equality of all population distribution functions.

VanDerWaerden(double[]...) - Constructor for class dev.nm.stat.test.rank.VanDerWaerden

Perform the Van Der Waerden test to test for the equality of all population distribution functions.

VanDerWaerden1969 - Class in dev.nm.stat.random.rng.univariate.beta

Deprecated.
Cheng1978 is a much better algorithm.

VanDerWaerden1969(RandomGammaGenerator, RandomGammaGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969

Deprecated.

Construct a random number generator to sample from the beta distribution.

VanDerWaerden1969(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.beta.VanDerWaerden1969

Deprecated.

Construct a random number generator to sample from the beta distribution.

var() - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix

Gets the variance of the elements.

var(int) - Method in class dev.nm.stat.descriptive.correlation.CorrelationMatrix

Gets the variance of the i-th element.

var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast

Gets the variance of the prediction.

var() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep

Gets the mean squared error of the h-step ahead prediction.

var() - Method in interface dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAFit

Get the variance of the white noise.

var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep

Gets the mean squared error of the h-step ahead prediction.

var(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep

Gets the mean squared error of the prediction at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).

var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep

Gets the mean squared error of the one-step ahead prediction.

var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ConditionalSumOfSquares

var() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.garch.GARCHModel

Compute the unconditional variance of the GARCH model.

var(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm

Gets the mean squared error for prediction errors at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).

var - Variable in class tech.nmfin.trend.dai2011.Dai2011HMM.CalibrationParam

VARFit - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

This class construct a VAR model by estimating the coefficients using OLS regression.

VARFit(MultivariateIntTimeTimeSeries, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARFit

Estimate a VAR model from a multivariate time series.

Variable(String, int) - Constructor for class dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization.SOCPPortfolioConstraint.Variable

Constructs a named variable.

variance() - Method in class dev.nm.stat.descriptive.moment.Kurtosis

Get the (unbiased) variance.

variance() - Method in class dev.nm.stat.descriptive.moment.Skewness

Get the (unbiased) variance.

Variance - Class in dev.nm.stat.descriptive.moment

The variance of a sample is the average squared deviations from the sample mean.

Variance() - Constructor for class dev.nm.stat.descriptive.moment.Variance

Construct an empty Variance calculator.

Variance(double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.Variance

Construct a Variance calculator, initialized with a sample.

Variance(double[]) - Constructor for class dev.nm.stat.descriptive.moment.Variance

Construct an unbiased Variance calculator.

Variance(Variance) - Constructor for class dev.nm.stat.descriptive.moment.Variance

Copy constructor.

variance() - Method in class dev.nm.stat.distribution.univariate.BetaDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.BinomialDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.ChiSquareDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.EmpiricalDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.ExponentialDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.FDistribution

Gets the variance of this distribution.

variance() - Method in class dev.nm.stat.distribution.univariate.GammaDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.LogNormalDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.NormalDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.PoissonDistribution

variance() - Method in interface dev.nm.stat.distribution.univariate.ProbabilityDistribution

Gets the variance of this distribution.

variance() - Method in class dev.nm.stat.distribution.univariate.RayleighDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.TDistribution

Gets the variance of this distribution.

variance() - Method in class dev.nm.stat.distribution.univariate.TriangularDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.TruncatedNormalDistribution

variance() - Method in class dev.nm.stat.distribution.univariate.WeibullDistribution

variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedEVD

variance() - Method in class dev.nm.stat.evt.evd.univariate.GeneralizedParetoDistribution

\[ \frac{\sigma^2}{(1-\xi)^2(1-2\xi)} \] for \(\xi < 1/2\).

variance() - Method in class dev.nm.stat.evt.evd.univariate.MaximaDistribution

variance() - Method in class dev.nm.stat.evt.evd.univariate.MinimaDistribution

variance() - Method in class dev.nm.stat.evt.evd.univariate.OrderStatisticsDistribution

variance() - Method in interface dev.nm.stat.random.Estimator

Gets the variance of the estimator.

variance() - Method in class dev.nm.stat.random.sampler.resampler.BootstrapEstimator

Gets the estimator variance, of which the convergence limit is decided by sample size, not B.

variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMBinomial

variance(double) - Method in interface dev.nm.stat.regression.linear.glm.distribution.GLMExponentialDistribution

The variance function of the distribution in terms of the mean μ.

variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGamma

variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMGaussian

variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMInverseGaussian

variance(double) - Method in class dev.nm.stat.regression.linear.glm.distribution.GLMPoisson

variance() - Method in class dev.nm.stat.stochasticprocess.univariate.integration.IntegralExpectation

Compute the variance of the integral.

variance() - Method in class dev.nm.stat.stochasticprocess.univariate.random.ExpectationAtEndTime

Get the variance.

variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovDistribution

Deprecated.

variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

Deprecated.

variance() - Method in class dev.nm.stat.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

Deprecated.

variance() - Method in class dev.nm.stat.test.distribution.normality.ShapiroWilkDistribution

Deprecated.

variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

variance() - Method in class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

VariancebtX - Class in dev.nm.algebra.linear.matrix.doubles.operation

Computes \(b'Xb\).

VariancebtX(Vector, Matrix) - Constructor for class dev.nm.algebra.linear.matrix.doubles.operation.VariancebtX

Computes \(b'Xb\).

variation() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

Gets the H variation/fluctuation.

VARIMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.arima

An ARIMA(p, d, q) process, Y_t, is such that \[ X_t = (1 - L)^d Y_t \] where L is the lag operator, d the order of difference, X_t an ARMA(p, q) process, for which \[ X_t = \mu + \Sigma \phi_i X_{t-i} + \Sigma \theta_j \epsilon_{t-j} + \epsilon_t, \] X_t, μ and ε_t are n-dimensional vectors.

VARIMAModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Construct a multivariate ARIMA model.

VARIMAModel(Vector, Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Construct a multivariate ARIMA model with unit variance.

VARIMAModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Construct a multivariate ARIMA model with zero-intercept (mu).

VARIMAModel(Matrix[], int, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Construct a multivariate ARIMA model with unit variance and zero-intercept (mu).

VARIMAModel(ARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Construct a multivariate model from a univariate ARIMA model.

VARIMAModel(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

Copy constructor.

VARIMASim - Class in dev.nm.stat.timeseries.linear.multivariate.arima

This class simulates a multivariate ARIMA process.

VARIMASim(VARIMAModel, Vector[], Vector[], RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim

Construct a multivariate ARIMA model.

VARIMASim(VARIMAModel, RandomVectorGenerator) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim

Construct a multivariate ARIMA model.

VARIMASim(VARIMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMASim

Construct a multivariate ARIMA model, using random standard Gaussian innovations.

VARIMAXModel - Class in dev.nm.stat.timeseries.linear.multivariate.arima

The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables.

VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Construct a multivariate ARIMAX model.

VARIMAXModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Construct a multivariate ARIMAX model with unit variance.

VARIMAXModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Construct a multivariate ARIMAX model with zero-intercept (mu).

VARIMAXModel(Matrix[], int, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).

VARIMAXModel(VARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Copy constructor.

VARIMAXModel(ARIMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

Construct a multivariate ARIMAX model from a univariate ARIMAX model.

VARLinearRepresentation - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms.

VARLinearRepresentation(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation

Construct the linear representation of an ARMA model.

VARLinearRepresentation(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARLinearRepresentation

Construct the linear representation of an ARMA model up to the default number of lags VARLinearRepresentation.DEFAULT_NUMBER_OF_LAGS.

VARMAAutoCorrelation - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.

VARMAAutoCorrelation(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation

Compute the auto-correlation function for a vector ARMA model.

VARMAAutoCovariance - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.

VARMAAutoCovariance(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCovariance

Compute the auto-covariance function for a vector ARMA model.

VARMAForecastOneStep - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

This is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor.

VARMAForecastOneStep(MultivariateIntTimeTimeSeries, VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep

Construct an instance of InnovationAlgorithm for a multivariate ARMA time series.

VARMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

A multivariate ARMA model, X_t, takes this form.

VARMAModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Construct a multivariate ARMA model.

VARMAModel(Vector, Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Construct a multivariate ARMA model with unit variance.

VARMAModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Construct a multivariate ARMA model with zero-intercept (mu).

VARMAModel(Matrix[], Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Construct a multivariate ARMA model with unit variance and zero-intercept (mu).

VARMAModel(ARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Construct a multivariate model from a univariate ARMA model.

VARMAModel(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

Copy constructor.

VARMAXModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

The VARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables.

VARMAXModel(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Construct a multivariate ARMAX model.

VARMAXModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Construct a multivariate ARMAX model with unit variance.

VARMAXModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Construct a multivariate ARMAX model with zero-intercept (mu).

VARMAXModel(Matrix[], Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).

VARMAXModel(ARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Construct a multivariate model from a univariate ARMAX model.

VARMAXModel(VARMAXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAXModel

Copy constructor.

VARModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

This class represents a VAR model.

VARModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Construct a VAR model.

VARModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Construct a VAR model with unit variance.

VARModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Construct a VAR model with zero-intercept (mu).

VARModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Construct a VAR model with unit variance and zero-intercept (mu).

VARModel(ARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Construct a multivariate model from a univariate AR model.

VARModel(VARModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

Copy constructor.

VARXModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

A VARX (Vector AutoRegressive model with eXogeneous inputs) model, X_t, takes this form.

VARXModel(Vector, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX model.

VARXModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX model with unit variance.

VARXModel(Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX model with zero-mean.

VARXModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX model with unit variance and zero-mean.

VARXModel(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX(p) from a transitory VECM(p).

VARXModel(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Construct a VARX(p) from a long-run VECM(p).

VARXModel(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARXModel

Copy constructor.

VECM - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

A Vector Error Correction Model (VECM(p)) has one of the following specifications:
Transitory: \[ \Delta Y_t = \mu + \Pi Y_{t-1} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1 \] or
Long-run: \[ \Delta Y_t = \mu + \Pi Y_{t-p} + \sum \left ( \Gamma_i Y_{t-1} \right ) + \Psi D_t + \epsilon_t, i = 1, 2, ..., p-1 \] Y_t, μ and ε_t are n-dimensional vectors.

VECM(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Construct a VECM(p) model.

VECM(VECM) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECM

Copy constructor.

VECMLongrun - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

The long-run Vector Error Correction Model (VECM(p)) takes this form.

VECMLongrun(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun

Construct a long-run VECM(p) model.

VECMLongrun(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun

Construct a long-run VECM(p) model with zero-intercept (mu).

VECMLongrun(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun

Construct a long-run VECM(p) from a VARX(p).

VECMLongrun(VECMLongrun) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMLongrun

Copy constructor.

VECMTransitory - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

A transitory Vector Error Correction Model (VECM(p)) takes this form.

VECMTransitory(Vector, Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory

Construct a transitory VECM(p) model.

VECMTransitory(Matrix, Matrix[], Matrix, Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory

Construct a transitory VECM(p) model with zero-intercept (mu).

VECMTransitory(VARXModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory

Construct a transitory VECM(p) from a VARX(p).

VECMTransitory(VECMTransitory) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VECMTransitory

Copy constructor.

Vector - Interface in dev.nm.algebra.linear.vector.doubles

An Euclidean vector is a geometric object that has both a magnitude/length and a direction.

VectorAccessException - Exception in dev.nm.algebra.linear.vector

This is the exception thrown when any invalid access to a Vector instance is detected, e.g., out-of-range index.

VectorAccessException(int, int) - Constructor for exception dev.nm.algebra.linear.vector.VectorAccessException

Constructs an instance of VectorAccessException for out-of-range access.

VectorAccessException(String) - Constructor for exception dev.nm.algebra.linear.vector.VectorAccessException

Constructs an instance of VectorAccessException.

VectorFactory - Class in dev.nm.algebra.linear.vector.doubles.operation

These are the utility functions that create new instances of vectors from existing ones.

VectorMathOperation - Class in dev.nm.algebra.linear.vector.doubles.dense

This is a generic implementation of the math operations of double based Vector.

VectorMathOperation() - Constructor for class dev.nm.algebra.linear.vector.doubles.dense.VectorMathOperation

VectorMonitor - Class in dev.nm.misc.algorithm.iterative.monitor

This IterationMonitor stores all vectors generated during iterations.

VectorMonitor() - Constructor for class dev.nm.misc.algorithm.iterative.monitor.VectorMonitor

VectorSizeMismatch - Exception in dev.nm.algebra.linear.vector

This is the exception thrown when an operation is performed on two vectors with different sizes.

VectorSizeMismatch(int, int) - Constructor for exception dev.nm.algebra.linear.vector.VectorSizeMismatch

Constructs an instance of SizeMismatch.

VectorSpace<V,F extends Field<F>> - Interface in dev.nm.algebra.structure

A vector space is a set V together with two binary operations that combine two entities to yield a third, called vector addition and scalar multiplication.

vers(double) - Static method in class dev.nm.geometry.TrigMath

Returns the versed sine or versine of an angle.

vertex() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

Gets the node.

VertexTree<T> - Class in dev.nm.graph.type

A VertexTree is both a tree and a vertex/node.This implementation builds a tree incrementally and recursively (combining trees).

VertexTree(T) - Constructor for class dev.nm.graph.type.VertexTree

vertices() - Method in interface dev.nm.geometry.polyline.PolygonalChain

Get a list of the vertices defining the chain.

vertices() - Method in class dev.nm.geometry.polyline.PolygonalChainByArray

vertices() - Method in interface dev.nm.graph.Graph

Gets the set of all vertices in this graph.

vertices() - Method in interface dev.nm.graph.HyperEdge

Gets the set of vertices associated with the edge.

vertices() - Method in class dev.nm.graph.type.SimpleArc

vertices() - Method in class dev.nm.graph.type.SimpleEdge

vertices() - Method in class dev.nm.graph.type.SparseDiGraph

Gets the set of all vertices in this graph, sorted by the number of parents.

vertices() - Method in class dev.nm.graph.type.SparseGraph

vertices() - Method in class dev.nm.graph.type.SparseTree

vertices() - Method in class dev.nm.graph.type.VertexTree

visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.GSATemperatureFunction

visitingTemperature(int) - Method in class dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.SimpleTemperatureFunction

visitingTemperature(int) - Method in interface dev.nm.solver.multivariate.unconstrained.annealing.temperaturefunction.TemperatureFunction

Gets the visiting temperature \(T^V_t\) at time t.

visitTime() - Method in class dev.nm.graph.algorithm.traversal.GraphTraversal.Node

Gets the first visit time of this node.

Viterbi - Class in dev.nm.stat.hmm

The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models.

Viterbi(HiddenMarkovModel) - Constructor for class dev.nm.stat.hmm.Viterbi

Constructs an Viterbi algorithm for an HMM.

VMAInvertibility - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages.

VMAInvertibility(VARMAModel, int) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility

Construct the inverse representation of an ARMA model.

VMAInvertibility(VARMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAInvertibility

Construct the inverse representation of an ARMA model up to the default number of lags VMAInvertibility.DEFAULT_NLAGS.

VMAModel - Class in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma

This class represents a multivariate MA model.

VMAModel(Vector, Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Construct a multivariate MA model.

VMAModel(Vector, Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Construct a multivariate MA model with unit variance.

VMAModel(Matrix[], Matrix) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Construct a multivariate MA model with zero-mean.

VMAModel(Matrix[]) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Construct a multivariate MA model with unit variance and zero-mean.

VMAModel(MAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Construct a multivariate MA model from a univariate MA model.

VMAModel(VMAModel) - Constructor for class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VMAModel

Copy constructor.

volatility() - Method in class tech.nmfin.meanreversion.hvolatility.HConstruction

Gets the H volatility.

Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.BidiagonalSVDbyMR3

Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3.SVDbyMR3

Vt() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.householder.HouseholderInPlace

Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.

W

W(Vector, Vector) - Method in class dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint.SQPASEVariation1

Compute W.

W(int) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Gets W(t), the covariance matrix of w_t.

W(int) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Get W(t), the variance of w_t.

w0 - Variable in class tech.nmfin.portfoliooptimization.lai2010.optimizer.MVOptimizerMinWeights

wA() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the weighted regressor matrix.

WaveEquation1D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1

A one-dimensional wave equation is a hyperbolic PDE that takes the following form.

WaveEquation1D(double, double, double, UnivariateRealFunction, UnivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1.WaveEquation1D

Constructs an one-dimensional wave equation.

WaveEquation2D - Class in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2

A two-dimensional wave equation is a hyperbolic PDE that takes the following form.

WaveEquation2D(double, double, double, double, BivariateRealFunction, BivariateRealFunction) - Constructor for class dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2.WaveEquation2D

Create a two-dimensional wave equation.

WeibullDistribution - Class in dev.nm.stat.distribution.univariate

The Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2), where k is the shape parameter.

WeibullDistribution(double, double) - Constructor for class dev.nm.stat.distribution.univariate.WeibullDistribution

Construct a Weibull distribution.

WeibullRNG - Class in dev.nm.stat.random.rng.univariate

This random number generator samples from the Weibull distribution using the inverse transform sampling method.

WeibullRNG(double, double) - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG

Construct a random number generator to sample from the Weibull distribution.

WeibullRNG() - Constructor for class dev.nm.stat.random.rng.univariate.WeibullRNG

Construct a random number generator to sample from the Weibull distribution.

weight() - Method in class tech.nmfin.portfoliooptimization.clm.TurningPoint

WeightedArc<V> - Interface in dev.nm.graph

A weighted arc is an arc that has a weight or a cost associated with it.

WeightedEdge<V> - Interface in dev.nm.graph

A weighted edge has a weight or a cost associated with it.

weightedFittedValues() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the weighted, fitted values.

WeightedMean - Class in dev.nm.stat.descriptive.moment.weighted

The weighted mean is defined as \[ \bar{x} = \frac{ \sum_{i=1}^N w_i x_i}{\sum_{i=1}^N w_i} \]

WeightedMean() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean

WeightedMean(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedMean

weightedResiduals() - Method in class dev.nm.stat.regression.linear.residualanalysis.LMResiduals

Gets the weighted residuals.

WeightedRSS - Class in dev.nm.stat.regression

Weighted sum of squared residuals (RSS) for a given function \(f(.)\) and observations \((x_i,y_i)\).

WeightedRSS(UnivariateRealFunction) - Constructor for class dev.nm.stat.regression.WeightedRSS

Constructs a calculator to compute the weighted RSS for a given function.

WeightedVariance - Class in dev.nm.stat.descriptive.moment.weighted

The weighted sample variance is defined as follows.

WeightedVariance() - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

WeightedVariance(boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

WeightedVariance(double[], double[]) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

WeightedVariance(double[], double[], boolean) - Constructor for class dev.nm.stat.descriptive.moment.weighted.WeightedVariance

weights - Variable in class dev.nm.solver.multivariate.constrained.general.penaltymethod.MultiplierPenalty

the weights for the constraints

weights() - Method in interface dev.nm.stat.regression.linear.glm.GLMFitting

Gets the weights assigned to the observations.

weights() - Method in class dev.nm.stat.regression.linear.glm.IWLS

weights() - Method in class dev.nm.stat.regression.linear.glm.quasi.QuasiGLMNewtonRaphson

weights() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the weights assigned to each observation.

weights() - Method in class tech.nmfin.portfoliooptimization.lai2010.Lai2010NPEBModel.OptimalWeights

which(double[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils

Get the indices of the array elements which satisfy the boolean test.

which(int[], DoubleUtils.which) - Static method in class dev.nm.number.DoubleUtils

Get the indices of the array elements which satisfy the boolean test.

White - Class in dev.nm.stat.test.regression.linear.heteroskedasticity

The White test tests for conditional heteroskedasticity.

White(LMResiduals) - Constructor for class dev.nm.stat.test.regression.linear.heteroskedasticity.White

Perform the White test to test for heteroskedasticity in a linear regression model.

WilcoxonRankSum - Class in dev.nm.stat.test.rank.wilcoxon

The Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset.

WilcoxonRankSum(double[], double[], double, boolean, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.

WilcoxonRankSum(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.

WilcoxonRankSum(double[], double[], double) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations, or whether the means differ by an offset.

WilcoxonRankSum(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSum

Perform the Wilcoxon Rank Sum test to test for the equality of means of two populations.

WilcoxonRankSumDistribution - Class in dev.nm.stat.test.rank.wilcoxon

Compute the exact distribution of the Wilcoxon rank sum test statistic.

WilcoxonRankSumDistribution(int, int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonRankSumDistribution

Construct a Wilcoxon Rank Sum distribution for sample sizes M and N.

WilcoxonSignedRank - Class in dev.nm.stat.test.rank.wilcoxon

The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and for the two-sample case, the equality of medians of groups.

WilcoxonSignedRank(double[], double[], double, boolean) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[], double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[], int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRank(double[]) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRank

Perform the Wilcoxon Signed Rank test to test for the equality of medians.

WilcoxonSignedRankDistribution - Class in dev.nm.stat.test.rank.wilcoxon

Compute the exact distribution of the Wilcoxon signed rank test statistic.

WilcoxonSignedRankDistribution(int) - Constructor for class dev.nm.stat.test.rank.wilcoxon.WilcoxonSignedRankDistribution

Construct a Wilcoxon Signed Rank distribution for a sample size N.

withInitialGuess(Vector) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Overrides the initial guess of the solution.

withLeftPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Overrides the left preconditioner.

withMaxIteration(int) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Overrides the maximum count of iterations.

withNewLength(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.InnovationsAlgorithm

withRightPreconditioner(Preconditioner) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Overrides the right preconditioner.

withTolerance(Tolerance) - Method in class dev.nm.algebra.linear.matrix.doubles.linearsystem.LSProblem

Overrides the tolerance instance.

Wt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFtWt

Get the current value(s) of the driving Brownian motion(s).

Wt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.FtWt

Get the current value of the driving Brownian motion.

wy() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the weighted response vector.

X

x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Get the value of x_i, the i-th value of the independent variable x.

x() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Get the values of the independent variable x_i.

x() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

x(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid

x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid

x(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid

Get the value of the independent variable x_i at the given index.

x(int) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid

Get all the values of the independent variable x_i as an array.

x(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

x(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

x() - Method in class dev.nm.analysis.curvefit.interpolation.NevilleTable

Get a copy of the x's.

x() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution

Get the values of the independent variable.

x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D

Gets the value on the x-axis at index k.

x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid1D

Gets the value on the space axis at index j.

x(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the value on the x-axis at index i.

x() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction

x() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs

Get the abscissae.

x() - Method in class dev.nm.analysis.function.tuple.Pair

x

x() - Method in class dev.nm.analysis.function.tuple.PartialFunction

x() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs

x() - Method in class dev.nm.analysis.function.tuple.Triple

Return the value of the first element of the triple.

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.DoubleExponential

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.Exponential

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.InvertingVariable

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.PowerLawSingularity

x() - Method in class dev.nm.analysis.integration.univariate.riemann.substitution.StandardInterval

x() - Method in interface dev.nm.analysis.integration.univariate.riemann.substitution.SubstitutionRule

the transformation: x(t)

x() - Method in exception dev.nm.analysis.root.univariate.NoRootFoundException

the best approximate root found so far

X - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath

This is the minimizer for the primal problem.

x - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution

This is the minimizer for the primal problem.

x() - Method in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.RealScalarFunctionChromosome

Get the candidate solution.

X() - Method in interface dev.nm.stat.factor.pca.PCA

Gets the (possibly centered and/or scaled) data matrix X used for the PCA.

x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging.DynamicsState

Gets the position.

x() - Method in class dev.nm.stat.random.rng.multivariate.mcmc.hybrid.LeapFrogging

Gets the current position.

X() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the factor matrix.

X() - Method in class tech.nmfin.infantino2010.Infantino2010PCA.Signal

x0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

Get the value of x₀, the first value of the independent variable x.

x0(int) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

Get the value of \(\mathbf{x_i}_0\), the first value of the independent variable \(x_i\).

x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE

Get the start point of the integrating interval [x₀, x₁].

x0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Gets the start point of the integrating interval [x₀, x₁].

x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE

Get the end point of the integrating interval [x₀, x₁].

x1() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Gets the end point of the integrating interval [x₀, x₁].

x_F - Variable in class dev.nm.stat.covariance.nlshrink.quest.QuEST.Result

transform zeta_list back from abscissa; it is same as whole_x_list; just follow the R code.

xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAForecastOneStep

Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.

xHat(int) - Method in class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.MultivariateForecastOneStep

Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.

xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecast.Forecast

Gets the prediction at time t.

xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep

The next h-step ahead prediction.

xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastMultiStep

Gets the h-step ahead prediction of the time series.

xHat(int) - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep

Gets the one-step ahead prediction \(\hat{x}_{n+1}\).

xHat() - Method in class dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep

Gets the one-step ahead prediction of the time series.

xi(HiddenMarkovModel, int[], ForwardBackwardProcedure) - Static method in class dev.nm.stat.hmm.discrete.BaumWelch

Gets the ξ matrices, where for 1 ≤ t ≤ T - 1, the t-th entry of ξ is an (N * N) matrix, for which the (i, j)-th entry is ξ_t(i, j).

XiTanLiu2010a - Class in dev.nm.stat.random.rng.univariate.gamma

Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.

XiTanLiu2010a(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a

Construct a random number generator to sample from the gamma distribution.

XiTanLiu2010a(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010a

Construct a random number generator to sample from the gamma distribution.

XiTanLiu2010b - Class in dev.nm.stat.random.rng.univariate.gamma

Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable.

XiTanLiu2010b(double, RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b

Construct a random number generator to sample from the gamma distribution.

XiTanLiu2010b(double) - Constructor for class dev.nm.stat.random.rng.univariate.gamma.XiTanLiu2010b

Construct a random number generator to sample from the gamma distribution.

xl - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the lower bound of the bracketing interval

XL2Norm() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the L2 norms of the covariates (a vector of ones if no standardization is required).

XLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the matrix of covariates (possibly demeaned and/or scaled) to be used in LARS.

XMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the mean vector to be subtracted from the covariates (a vector of zeros if no intercept is included).

xmin - Variable in class dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.SimpleGridMinimizer.Solution

xmin - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the best minimizer found so far

xnext - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the next best guess of the minimizer

xnext() - Method in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

Compute the next best estimate within the bracketing interval.

xnext() - Method in class dev.nm.solver.univariate.bracketsearch.BrentMinimizer.Solution

xnext() - Method in class dev.nm.solver.univariate.bracketsearch.FibonaccMinimizer.Solution

xnext() - Method in class dev.nm.solver.univariate.bracketsearch.GoldenMinimizer.Solution

xShifted(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep

Gets the shifted time series of observations.

xt(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Evaluates the state equation.

xt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Evaluates the state equation without the control variable.

xt(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Evaluate the state equation.

xt(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Evaluate the state equation without the control variable.

Xt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Get the current value of the stochastic process.

Xt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft

Get the current value of the stochastic process.

xt_mean(int, Vector, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Predicts the next state.

xt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Predicts the next state without control variable.

xt_mean(int, double, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Predict the next state.

xt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Predict the next state without control variable.

xt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateStateEquation

Gets the variance of the apriori prediction for the next state.

xt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.StateEquation

Get the variance of the apriori prediction for the next state.

XtAdaptedFunction - Class in dev.nm.stat.stochasticprocess.univariate.sde

This represents an F_t-adapted function that depends only on X(t).

XtAdaptedFunction() - Constructor for class dev.nm.stat.stochasticprocess.univariate.sde.XtAdaptedFunction

xu - Variable in class dev.nm.solver.univariate.bracketsearch.BracketSearchMinimizer.Solution

the upper bound of the bracketing interval

Y

y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

y(int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Get the value of y_j, the j-th value of the independent variable y.

y() - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Get the values of the independent variable y_j.

y() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

y(int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateArrayGrid

y(int...) - Method in interface dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateGrid

Get the value of the dependent variable y at the given indices in the grid.

y(int...) - Method in class dev.nm.analysis.curvefit.interpolation.multivariate.MultivariateRegularGrid

y() - Method in class dev.nm.analysis.differentialequation.ode.ivp.solver.ODESolution

Get the corresponding values of the dependent variable.

y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionGrid2D

Gets the value on the y-axis at index j.

y(int) - Method in interface dev.nm.analysis.differentialequation.pde.finitedifference.PDESolutionTimeSpaceGrid2D

Get the value on the y-axis at index j.

y() - Method in class dev.nm.analysis.function.rn2r1.univariate.StepFunction

y() - Method in interface dev.nm.analysis.function.tuple.OrderedPairs

Get the ordinates.

y() - Method in class dev.nm.analysis.function.tuple.Pair

y

y() - Method in class dev.nm.analysis.function.tuple.PartialFunction

y() - Method in class dev.nm.analysis.function.tuple.SortedOrderedPairs

y() - Method in class dev.nm.analysis.function.tuple.Triple

Return the value of the second element of the triple.

y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing.CentralPath

This is the maximizer for the dual problem.

y - Variable in class dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint.PrimalDualSolution

This is the maximizer for the dual problem.

y() - Method in class dev.nm.stat.regression.linear.LMProblem

Gets the response vector, the regressands, the dependent variables.

y(int) - Method in class dev.nm.stat.timeseries.linear.univariate.arima.ARIMAForecastMultiStep

Gets the stationary arma series.

y0() - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

Get the value of y₀, the first value of the independent variable y.

y0(int) - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE

Get the initial value for the n-th order (derivative).

y0() - Method in class dev.nm.analysis.differentialequation.ode.ivp.problem.ODE1stOrder

Gets the initial value of y, that is, y₀.

yes(double) - Method in interface dev.nm.number.DoubleUtils.ifelse

Return value for a true element of test.

yLARS() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the vector of response variable (possibly demeaned) to be used in LARS.

yMean() - Method in class dev.nm.stat.regression.linear.lasso.lars.LARSProblem

Gets the mean to be subtracted from the response variable (0 if no intercept is included).

yt(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Evaluates the observation equation.

yt(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation

Evaluate the observation equation.

yt_mean(int, Vector) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Predicts the next observation.

yt_mean(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation

Predict the next observation.

yt_var(int, Matrix) - Method in class dev.nm.stat.dlm.multivariate.MultivariateObservationEquation

Gets the covariance of the apriori prediction for the next observation.

yt_var(int, double) - Method in class dev.nm.stat.dlm.univariate.ObservationEquation

Get the variance of the apriori prediction for the next observation.

Z

z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateArrayGrid

z(int, int) - Method in interface dev.nm.analysis.curvefit.interpolation.bivariate.BivariateGrid

Get the value of the dependent variable z at the given indices in the grid.

z(int, int) - Method in class dev.nm.analysis.curvefit.interpolation.bivariate.BivariateRegularGrid

z() - Method in class dev.nm.analysis.function.tuple.Triple

Return the value of the third element of the triple.

Z1() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

Get Z1.

Z2() - Method in class dev.nm.stat.test.distribution.normality.DAgostino

Get Z1.

ZangwillImpl(C2OptimProblem) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer.ZangwillImpl

ZangwillMinimizer - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection

Zangwill's algorithm is an improved version of Powell's algorithm.

ZangwillMinimizer(double, double, int) - Constructor for class dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection.ZangwillMinimizer

Construct a multivariate minimizer using the Zangwill method.

ZangwillMinimizer.ZangwillImpl - Class in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection

an implementation of Zangwill's algorithm

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.ImmutableMatrix

ZERO() - Method in interface dev.nm.algebra.linear.matrix.doubles.MatrixRing

Get a zero matrix that has the same dimension as this matrix.

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.GivensMatrix

Deprecated.
no zero matrix for GivensMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.PermutationMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.CSRSparseMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.DOKSparseMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.LILSparseMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.SparseVector

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.ColumnBindMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.DiagonalSum

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.FastKroneckerProduct

ZERO() - Method in class dev.nm.algebra.linear.matrix.doubles.operation.SubMatrixRef

ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.ComplexMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.GenericFieldMatrix

ZERO() - Method in class dev.nm.algebra.linear.matrix.generic.matrixtype.RealMatrix

ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.dense.DenseVector

ZERO() - Method in class dev.nm.algebra.linear.vector.doubles.ImmutableVector

ZERO() - Method in interface dev.nm.algebra.linear.vector.doubles.Vector

Get a 0-vector that has the same length as this vector.

ZERO() - Method in interface dev.nm.algebra.structure.AbelianGroup

The additive element 0 in the group, such that for all elements a in the group, the equation 0 + a = a + 0 = a holds.

ZERO - Static variable in class dev.nm.analysis.function.polynomial.Polynomial

a polynomial representing 0

ZERO() - Method in class dev.nm.analysis.function.polynomial.Polynomial

ZERO - Static variable in class dev.nm.number.complex.Complex

a number representing 0.0 + 0.0i

ZERO() - Method in class dev.nm.number.complex.Complex

Get zero - the number representing 0.0 + 0.0i.

ZERO - Static variable in class dev.nm.number.Real

a number representing 0

ZERO() - Method in class dev.nm.number.Real

ZERO_ENTRY - Static variable in class dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.DenseData

ZeroDriftVector - Class in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients

This class represents a 0 drift function.

ZeroDriftVector() - Constructor for class dev.nm.stat.stochasticprocess.multivariate.sde.coefficients.ZeroDriftVector

ZeroPenalty - Class in dev.nm.solver.multivariate.constrained.general.penaltymethod

This is a dummy zero cost (no cost) penalty function.

ZeroPenalty(int) - Constructor for class dev.nm.solver.multivariate.constrained.general.penaltymethod.ZeroPenalty

Construct a no-cost penalty function.

Ziggurat2000 - Class in dev.nm.stat.random.rng.univariate.normal

The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution.

Ziggurat2000(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000

Construct a Ziggurat random normal generator.

Ziggurat2000() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Ziggurat2000

Construct a Ziggurat random normal generator.

Ziggurat2000Exp - Class in dev.nm.stat.random.rng.univariate.exp

This implements the ziggurat algorithm to sample from the exponential distribution.

Ziggurat2000Exp() - Constructor for class dev.nm.stat.random.rng.univariate.exp.Ziggurat2000Exp

Zignor2005 - Class in dev.nm.stat.random.rng.univariate.normal

This is an improved version of the Ziggurat algorithm as proposed in the reference.

Zignor2005(RandomLongGenerator) - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005

Construct an improved Ziggurat random normal generator.

Zignor2005() - Constructor for class dev.nm.stat.random.rng.univariate.normal.Zignor2005

Construct an improved Ziggurat random normal generator.

Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.random.MultivariateRandomProcess

Get a d-dimensional Gaussian innovation.

Zt() - Method in class dev.nm.stat.stochasticprocess.multivariate.sde.MultivariateFt

Get the current value of the Gaussian innovation.

Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.random.RandomProcess

Get a Gaussian innovation.

Zt() - Method in class dev.nm.stat.stochasticprocess.univariate.sde.Ft

Get the current value of the Gaussian innovation.

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