SuanShu, a Java numerical and statistical library
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D

D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
 
D() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.EigenDecomposition
Get a copy of the diagonal matrix D as in Q %*% D %*% Q' = A Note that we only support real eigenvalues for now.
D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
 
D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
 
D() - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVDDecomposition
Get a copy of D as in U' %*% A %*% V = D U %*% D %*% V' = A
D() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.LDL
Get a copy of the diagonal matrix D as in A = L %*% D %*% Lt
D() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.hessian.MatthewsDavies
Get a copy of the diagonal matrix D in the LDL decomposition.
d() - Method in class com.numericalmethod.suanshu.stats.dlm.Dlm
Get the dimension of observations.
d - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
the dimension of this Brownian motion
d - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
the dimension of the Brownian motion
d - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Bessel
the number of independent driving Brownian motions
d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
Get the order of integration.
d() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel
Get the order of integration.
d2f - Variable in class com.numericalmethod.suanshu.analysis.uniroot.Halley
the 2nd derivative of f, d2f/dx2
DAgostino - Class in com.numericalmethod.suanshu.stats.test.distribution.normality
D'Agostino's K2 test is a goodness-of-fit measure of departure from normality.
DAgostino(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
Perform D'Agostino's test to test for the departure from normality.
data - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
stores the values of matrix entries
dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator
 
dB(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
Get the Brownian increment at the t-th time grid point.
dB(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator
 
db(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
 
DBeta - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class represents the first order derivative function of the Beta function w.r.t x, i.e., dB(x, y)/dx.
DBeta() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
 
DBetaRegularized - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class represents the first order derivative function of the Regularized Incomplete Beta function w.r.t x, the upper limit.
DBetaRegularized(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
Construct the derivative function of the Regularized Incomplete Beta function with shape parameters p and q.
dBt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
Get all the Brownian increments.
deepCopy() - Method in class com.numericalmethod.suanshu.datastructure.list.MatrixList
Get a deep copy of this MatrixList instance.
deepCopy() - Method in class com.numericalmethod.suanshu.datastructure.list.VectorList
Get a deep copy of this VectorList instance.
deepCopy() - Method in interface com.numericalmethod.suanshu.DeepCopyable
The implementation can return 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 com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
Return 'this' as this Matrix is immutable.
deepCopy() - Method in interface com.numericalmethod.suanshu.matrix.doubles.Matrix
The implementation can return 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 com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
 
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct
Return 'this' as this Matrix is immutable.
deepCopy() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
Return 'this' as this Matrix is immutable.
deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
 
deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtWt
 
deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
 
deepCopy() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtWt
 
deepCopy() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
 
deepCopy() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
 
deepCopy() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
 
DeepCopyable - Interface in com.numericalmethod.suanshu
This interface provides a way to do polymorphic copying.
DEFAULT_LICENSE_FILES - Static variable in class com.numericalmethod.suanshu.license.License
Default license files
DEFAULT_NLAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Invertibility
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.LinearRepresentation
the default number of lags
DEFAULT_NUMBER_OF_LAGS - Static variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.LinearRepresentation
the default number of lags
DEFAULT_PENALTY_FUNCTION_FACTORY - Static variable in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyMethod
the default penalty function factory
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.QuasiMinimalResidualSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_RESIDUAL_REFRESH_RATE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.SteepestDescentSolver
The algorithm recomputes the residual as b - Axi once per this number of iterations
DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.AbsoluteTolerance
default tolerance
DEFAULT_TOLERANCE - Static variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.RelativeTolerance
default tolerance
deflationCriterion - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg
the deflation criterion used for this instance of Hessenberg
degree - Variable in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
the degree of this polynomial It is equal to the largest exponent of the variable.
DenseData - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
This class provides an implementation of a dense matrix.
DenseData(double[], DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
 
DenseData(double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
 
DenseData(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
 
DenseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
This class implements the standard double dense matrix representation.
DenseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Construct a matrix of dimension nRows * nCols.
DenseMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Construct a matrix from a 2D double[][] array.
DenseMatrix(double[], int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Construct a matrix from a 1D double[] array.
DenseMatrix(Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Construct a column matrix from a vector.
DenseMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Copy constructor performing a deep copy.
DenseMatrix(DenseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
Copy constructor performing a deep copy.
DenseMatrixUtils - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
 
DenseMatrixUtils() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrixUtils
 
DenseVector - Class in com.numericalmethod.suanshu.vector.doubles.dense
This class implements the standard double dense vector representation.
DenseVector(int) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
Construct a vector of length length.
DenseVector(int, double) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
Construct a vector of length length initialized with value value.
DenseVector(double...) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
Construct a vector from a 1D double array.
DenseVector(Matrix) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
Construct a vector from a column matrix.
DenseVector(Vector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
 
DenseVector(DenseVector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
Copy constructor performing a deep copy.
Densifiable - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
This interface indicates whether a matrix implementation can be efficiently converted to a standard dense matrix representation.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
This is the probability mass function for the discrete sample.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
 
density(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution
The density function, which, if exists, is the derivative of F.
density(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated. Not supported yet.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated. Not supported yet.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated. Not supported yet.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated. Not supported yet.
density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
density(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
DErf - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class represents the first order derivative function of the Error function, d(Erf) 2 ------ = --- exp(-x2) dx √π
DErf() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
 
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
 
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
 
derivative(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
 
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
Derivative of the link function, i.e., g'(x).
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Probit
 
derivative(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
Derivative of the link function, i.e., g'(x).
det() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.HilbertMatrix
The determinant of a Hilbert matrix is the reciprocal of an integer.
det(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Measure
Compute the determinant of a matrix.
determinant() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Jacobian
Compute the Jacobian determinant or simply the "Jacobian".
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
 
deviance(double, double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
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 com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Family
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 com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
 
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
 
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
 
deviance(double, double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
 
deviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
deviance
deviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals
the residual deviance
devianceResiduals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
deviances residuals
devianceResiduals - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals
the residuals, ε
deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.Residuals
 
deviances - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
deviances of observations
deviances() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.Residuals
 
df(double, double) - Method in class com.numericalmethod.suanshu.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 - Variable in class com.numericalmethod.suanshu.analysis.uniroot.Halley
the 1st derivative of f, df/dx
df - Variable in class com.numericalmethod.suanshu.analysis.uniroot.Newton
the 1st derivative of f, df/dx
df - Variable in class com.numericalmethod.suanshu.stats.test.mean.T
degree of freedom
df - Variable in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
the degree of freedom
df1 - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
the first degree of freedom
df1 - Variable in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
degree of freedoms
df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
degree of freedoms
df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
the degree of freedoms
df1 - Variable in class com.numericalmethod.suanshu.stats.test.variance.Levene
the degree of freedoms
df2 - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
the second degree of freedom
df2 - Variable in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
degree of freedoms
df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
degree of freedoms
df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
the degree of freedoms
df2 - Variable in class com.numericalmethod.suanshu.stats.test.variance.Levene
the degree of freedoms
Dfdx - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class computes the first order derivative function of a univariate function.
Dfdx(UnivariateRealFunction, Dfdx.Method) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
Construct the first order derivative function of a univariate function f.
Dfdx(UnivariateRealFunction) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
Construct, using finite difference, the first order derivative function of a univariate function f.
Dfdx.Method - Enum in com.numericalmethod.suanshu.analysis.differentiation.univariate
the methods available to compute numerical derivatives
DFFITS - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Diagnostics
DFFITS, Welsch and Kuh Measure
DFP - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems.
DFP() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.DFP
Construct an instance of DFP to minimize a function.
DGamma - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class represents the first order derivative function of the Gamma function.
DGamma() - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
 
DGaussian - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class computes the first order derivative function of the Gaussian function.
DGaussian(Gaussian) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
Construct the derivative function of the Gaussian function.
Dhat() - Method in class com.numericalmethod.suanshu.optimization.unconstrained.hessian.MatthewsDavies
Get a copy of the modified diagonal matrix which is positive definite.
Diagnostics - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
This class collects some diagnostics measures for the goodness of fit for an Ordinary Least Square linear regression model.
diagnostics - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OlsRegression
the diagnostic measures of this linear regression
diagonal(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Check if a square matrix is a diagonal matrix.
diagonal(Matrix) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
Take the diagonal of a matrix.
DiagonalMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
This class represents a matrix with non-zero entries only on the main diagonal.
DiagonalMatrix(double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Construct a diagonal matrix from a double[] array.
DiagonalMatrix(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Construct a diagonal matrix of dimension dim * dim.
DiagonalMatrix(DiagonalMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix
Copy constructor.
diagonalMatrix(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Take the diagonal of a matrix.
diff(double[], int, int) - Static method in class com.numericalmethod.suanshu.misc.R
Get the lagged and iterated differences.
diff(double[]) - Static method in class com.numericalmethod.suanshu.misc.R
Get the first differences of an array.
diff(double[][], int, int) - Static method in class com.numericalmethod.suanshu.misc.R
Get the lagged and iterated differences of vectors.
diff(double[][]) - Static method in class com.numericalmethod.suanshu.misc.R
Get the first differences of an array of vectors.
diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
Create a new and independent GenericTimeTimeSeries by taking the first difference.
diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
Construct a new and independent SimpleMultiVariateTimeSeries by taking the first difference.
diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
Create a new and independent GenericTimeTimeSeries by taking the first difference.
diff(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
Construct a new and independent SimpleTimeSeries by taking the first difference.
Diffusion - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
This represents the diffusion term, σ, of an SDE.
Diffusion - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
This class represents the diffusion term, σ, of a univariate SDE.
Digamma - Class in com.numericalmethod.suanshu.analysis.function.special
The digamma function is defined as the logarithmic derivative of the gamma function.
Digamma() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.Digamma
 
dim - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
the dimension of the square matrix A
dim - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
dimension; SymmetricMatrix is always square
dim() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.TriangularMatrix
Get the matrix dimension.
dim - Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
dimension of the square matrix
dim() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
Get the dimension of the process.
dimension - Variable in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
the dimension/coordinate to take from the output of f It counts from 1.
dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.ObservationEquation
Get the dimension of each observation y_t.
dimension() - Method in class com.numericalmethod.suanshu.stats.dlm.StateEquation
Get the dimension of each state x_t.
dimension() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
 
dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel
Get the dimension of multivariate time series.
dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Vecm
Get the dimension of multivariate time series.
dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
Get the dimension of the multivariate time series.
dimension() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries
Get the dimensionality of the multivariate time series.
dimension() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
 
dimension4Domain() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
Get the number of variables of the function.
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunctionFixedVariables
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.UnivariateRealFunction
 
dimension4Domain() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
 
dimension4Range() - Method in interface com.numericalmethod.suanshu.analysis.function.Function
Get the dimension of the range space the function.
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunctionFixedVariables
 
dimension4Range() - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.UnivariateRealFunction
 
dimension4Range() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
 
dimensionality - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.PenaltyFunction
dimensionality of the problem to be solved This is the number of free variables.
DimensionCheck - Class in com.numericalmethod.suanshu.matrix
This class collects the common functions for checking matrix dimensions.
DiscreteSampling<X> - Class in com.numericalmethod.suanshu.stats.sampling.discrete
This class samples from a discrete probability distribution.
DiscreteSampling(Iterable<X>, ProbabilityMassFunction<X>) - Constructor for class com.numericalmethod.suanshu.stats.sampling.discrete.DiscreteSampling
 
DiscretizedSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
This interface represents the discretized version of a multivariate SDE.
DiscretizedSDE - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
This interface represents the discretized version of a univariate SDE.
discriminant() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.BorderedHessian
Compute the determinant of the bordered Hessian at x.
discriminant() - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.Hessian
Compute the determinant of the Hessian at x, which is called the discriminant.
dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
 
dispersion(Vector, Vector, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
Different distribution models have different ways to compute dispersion, φ.
dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
 
dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
 
dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
 
dispersion(Vector, Vector, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
 
distribution - Variable in class com.numericalmethod.suanshu.stats.random.distribution.InverseTransformSampling
the distribution to generate random samples from
divide(F) - Method in interface com.numericalmethod.suanshu.mathstructure.Field
/ : F × F → F That is the same as this.multiply(that.inverse())
divide(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
 
divide(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
Compute the quotient of this complex number and that complex number.
divide(Real) - Method in class com.numericalmethod.suanshu.number.Real
 
divide(Real, int) - Method in class com.numericalmethod.suanshu.number.Real
/ : R × R → R Divide a Real number by another one.
divide(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
 
divide(DenseVector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
this / v Divide this by v, entry-by-entry.
divide(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
 
divide(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
this / that Divide this by that, entry-by-entry.
dk - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.SteepestDescent.LineSearch
the line search direction at the k-th iteration
Dlm - Class in com.numericalmethod.suanshu.stats.dlm
This class represents a controlled DLM (controlled dynamic linear model) specification.
Dlm(Vector, Matrix, ObservationEquation, StateEquation) - Constructor for class com.numericalmethod.suanshu.stats.dlm.Dlm
Construct a (multivariate) controlled dynamic linear model.
Dlm(Dlm) - Constructor for class com.numericalmethod.suanshu.stats.dlm.Dlm
Copy constructor.
DlmSim - Class in com.numericalmethod.suanshu.stats.dlm
This class simulates a (multivariate) controlled dynamic linear model process.
DlmSim(int, Dlm, MultiVariateTimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.dlm.DlmSim
Simulate a (multivariate) controlled dynamic linear model process.
DlmSim(int, Dlm) - Constructor for class com.numericalmethod.suanshu.stats.dlm.DlmSim
Simulate a (multivariate) dynamic linear model process.
dLogLikelihood(double[], int, int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.Garch
the gradient of the log-likelihood function for a set of observations The gradient log-likelihood takes θ as the inputs.
DokSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
The dictionary of key (DOK) format for sparse matrix.
DokSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
Create an instance of DOK sparse matrix with the matrix dimension.
DokSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
Create an instance of DOK sparse matrix with non-zero values.
DokSparseMatrix(int, int, List<SparseElement>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
Create an instance of DOK sparse matrix with a list of non-zero SparseElements.
DokSparseMatrix(DokSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
Copy constructor.
domain - Variable in class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem.IntegralConstraint
the domain to search for this particular integral variable
Doolittle - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
This class implements the Doolittle algorithm with column/partial pivoting for the LU decomposition of a square matrix.
Doolittle(Matrix, boolean, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
Construct an LU decomposition using the Doolittle algorithm.
Doolittle(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
Construct an LU decomposition using the Doolittle algorithm.
dotProduct(long[], long[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
Compute the element-wise multiplication between two long[]s.
dotProduct(double[], double[]) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
Compute the element-wise multiplication between two double[]s.
doubleArray() - Method in class com.numericalmethod.suanshu.datastructure.list.NumberList
Convert this list of numbers to a double array, if all its elements are real.
doubleArray2ArrayList(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert an ArrayList to a double[] array.
doubleArray2intArray(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert a double[] array to an int[] array.
doubleArray2StringArray(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Print out the numbers to a String buffer.
DoubleArrayOperation - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
 
DoubleExponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
This transformation speeds up the convergence of the Trapezoidal Rule exponentially.
DoubleExponential(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
Construct an instance of the DoubleExponential substitution rule.
DoubleExponential4HalfRealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
This Double Exponential transformation is good for the region (0, ∞).
DoubleExponential4HalfRealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4HalfRealLine
Construct an instance of DoubleExponential4HalfRealLine substitution rule.
DoubleExponential4RealLine - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
This Double Exponential transformation is good for the region (-∞, ∞).
DoubleExponential4RealLine(UnivariateRealFunction, double, double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential4RealLine
Construct an instance of DoubleExponential4RealLine substitution rule.
DoubleUtils - Class in com.numericalmethod.suanshu.number
This class collects the utility functions to manipulate data of types double and int.
DoubleUtils.RoundingScheme - Enum in com.numericalmethod.suanshu.number
the schemes available to round a number.
doubleValue() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
Create a DenseMatrix of double values from this complex matrix.
doubleValue() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
Create a DenseMatrix of double values from this real matrix.
doubleValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
 
doubleValue() - Method in class com.numericalmethod.suanshu.number.Real
 
doubleValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
 
DPolynomial - Class in com.numericalmethod.suanshu.analysis.differentiation.univariate
This class computes the first order derivative function of a polynomial, which, again, is a polynomial.
DPolynomial(Polynomial) - Constructor for class com.numericalmethod.suanshu.analysis.differentiation.univariate.DPolynomial
Construct the derivative function of a polynomial, which, again, is a polynomial.
Drift - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
This represents the drift term, μ, of an SDE.
Drift - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
This class represents the drift term, μ, of a univariate SDE.
drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
Create a new and independent GenericTimeTimeSeries by dropping the leading nItems entries, the most backward in time entries.
drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
Construct a new and independent SimpleMultiVariateTimeSeries by dropping the leading nItems entries.
drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
Create a new and independent GenericTimeTimeSeries by dropping the leading nItems entries, the most backward in time entries.
drop(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
Construct a new and independent SimpleTimeSeries by dropping the leading nItems entries.
dropTolerance(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
 
dropTolerance(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
 
dropTolerance(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
 
dropTolerance(double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseStructure
Remove non-zero entries x whose magnitude is less than or equal to the tolerance, i.e., (|x| <= tolerance).
dropTolerance(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
 
dt - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
the time differential
dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
Get the current time differential.
dt(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
Get the t-th time increment.
dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
Get all the time increments.
dt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
Get the current time differential.
du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
 
du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
 
du() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
Get an array of the measure values.
dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft
Get the increment of the driving Brownian motion during the time differential.
dWt() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Ft
Get the increment of the driving Brownian motion during the time differential.
dx - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Substitution
the first order derivative of the transformation x'(t) = dx(t)/dt
dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
 
dXt(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.DiscretizedSDE
This is the SDE specification of a stochastic process.
dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
This is the SDE specification of a stochastic process.
dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
 
dXt(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.DiscretizedSDE
This is the SDE specification of the stochastic process.
dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
This is the SDE specification of the stochastic process.
dXt(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Milstein
This is the SDE specification of the stochastic process.

SuanShu, a Java numerical and statistical library
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