SuanShu, a Java numerical and statistical library
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

C

c - Variable in class com.numericalmethod.suanshu.analysis.function.special.Gaussian
c as in f(x) = a * exp{-(x - b)2 / 2 / c2}
c - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential
a constant; usually either 0 or 0.5 * PI
c - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem1
the objective min c'x
c - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem2
the objective min c'x
C0() - Method in class com.numericalmethod.suanshu.stats.dlm.Dlm
Get the covariance matrix of x_0.
call(String, Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl
 
cbind(Vector...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Take an array of vectors and combine them by columns.
cbind(VectorList) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Take a list of vectors and combine them by columns.
cbind(Matrix...) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Take an array of matrices and combine them by columns.
cbind(MatrixList) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Take a list of matrices and combine them by columns.
ccdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
 
cdf(double) - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution
The cumulative distribution function.
cdf(double) - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
 
cdf(double) - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
 
centralMoment(int) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
Get the value of the k-th central moment.
ChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
Change of variable can easy the computation of some integrals, such as improper integrals.
ChangeOfVariable(Substitution, IterativeIntegrator) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
Construct an integrator that uses change of variable to do integration.
CharacteristicPolynomial - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
The characteristic polynomial of a square matrix is the function p(λ) = det (A - λI) The zeros of this polynomial are the eigenvalues of A.
CharacteristicPolynomial(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
Construct a CharacteristicPolynomial from a square matrix.
characteristicPolynomial() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
Get a copy of the characteristic polynomial.
checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
Check whether this LmProblem instance is valid.
checkInputs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.logistic.LogisticProblem
 
chi - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.linesearch.Fletcher
parameter chi ensures that the result is not too close to the boundary
ChiSquare4Independence - Class in com.numericalmethod.suanshu.stats.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.
ChiSquare4Independence(Matrix, int, ChiSquare4Independence.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
Assess whether the two random variable in the contingency table is independent.
ChiSquare4Independence(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
Assess whether the two random variable in the contingency table is independent.
ChiSquare4Independence.Type - Enum in com.numericalmethod.suanshu.stats.test.distribution.pearson
the distribution used for the test
ChiSquareDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
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 com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
Construct a Chi-Square distribution.
Cholesky - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L %*% Lt L is a lower triangular matrix.
Cholesky(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Cholesky
Run the Cholesky decomposition.
Cloglog - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the complementary log-log link function: g(x) = log(-log(1 - x))
Cloglog() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
 
coefficient(int) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Get the coefficient of xn-i, namely, an-i.
coefficients() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Get a copy of the coefficients.
CointegrationMle - Class in com.numericalmethod.suanshu.stats.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(SimpleMultiVariateTimeSeries, boolean, int, Matrix, JohansenAsymptoticDistribution.Test) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
Perform the Johansen procedure on a multi-variate time series.
CointegrationMle(SimpleMultiVariateTimeSeries, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
Perform the Johansen procedure on a multi-variate time series, using the EIGEN test.
CointegrationMle(SimpleMultiVariateTimeSeries, boolean) - Constructor for class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
Perform the Johansen procedure on a multi-variate time series.
colSums(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.MatrixUtils
 
columns(Matrix, int[]) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
CreateMatrix a sub-matrix from the columns of a matrix.
columns(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
CreateMatrix a sub-matrix from the columns of a matrix.
com.numericalmethod.suanshu - package com.numericalmethod.suanshu
 
com.numericalmethod.suanshu.algorithm - package com.numericalmethod.suanshu.algorithm
 
com.numericalmethod.suanshu.analysis.differentiation - package com.numericalmethod.suanshu.analysis.differentiation
 
com.numericalmethod.suanshu.analysis.differentiation.multivariate - package com.numericalmethod.suanshu.analysis.differentiation.multivariate
 
com.numericalmethod.suanshu.analysis.differentiation.univariate - package com.numericalmethod.suanshu.analysis.differentiation.univariate
 
com.numericalmethod.suanshu.analysis.function - package com.numericalmethod.suanshu.analysis.function
 
com.numericalmethod.suanshu.analysis.function.matrix - package com.numericalmethod.suanshu.analysis.function.matrix
 
com.numericalmethod.suanshu.analysis.function.polynomial - package com.numericalmethod.suanshu.analysis.function.polynomial
 
com.numericalmethod.suanshu.analysis.function.polynomial.root - package com.numericalmethod.suanshu.analysis.function.polynomial.root
 
com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub - package com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub
 
com.numericalmethod.suanshu.analysis.function.rn2r1 - package com.numericalmethod.suanshu.analysis.function.rn2r1
 
com.numericalmethod.suanshu.analysis.function.rn2rm - package com.numericalmethod.suanshu.analysis.function.rn2rm
 
com.numericalmethod.suanshu.analysis.function.special - package com.numericalmethod.suanshu.analysis.function.special
 
com.numericalmethod.suanshu.analysis.integration.univariate - package com.numericalmethod.suanshu.analysis.integration.univariate
 
com.numericalmethod.suanshu.analysis.integration.univariate.riemann - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann
 
com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution - package com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
 
com.numericalmethod.suanshu.analysis.interpolation - package com.numericalmethod.suanshu.analysis.interpolation
 
com.numericalmethod.suanshu.analysis.sequence - package com.numericalmethod.suanshu.analysis.sequence
 
com.numericalmethod.suanshu.analysis.uniroot - package com.numericalmethod.suanshu.analysis.uniroot
 
com.numericalmethod.suanshu.datastructure - package com.numericalmethod.suanshu.datastructure
 
com.numericalmethod.suanshu.datastructure.list - package com.numericalmethod.suanshu.datastructure.list
 
com.numericalmethod.suanshu.interval - package com.numericalmethod.suanshu.interval
 
com.numericalmethod.suanshu.license - package com.numericalmethod.suanshu.license
 
com.numericalmethod.suanshu.mathstructure - package com.numericalmethod.suanshu.mathstructure
 
com.numericalmethod.suanshu.matrix - package com.numericalmethod.suanshu.matrix
 
com.numericalmethod.suanshu.matrix.doubles - package com.numericalmethod.suanshu.matrix.doubles
 
com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization - package com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization
 
com.numericalmethod.suanshu.matrix.doubles.factorization.eigen - package com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
 
com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination - package com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination
 
com.numericalmethod.suanshu.matrix.doubles.factorization.qr - package com.numericalmethod.suanshu.matrix.doubles.factorization.qr
 
com.numericalmethod.suanshu.matrix.doubles.factorization.svd - package com.numericalmethod.suanshu.matrix.doubles.factorization.svd
 
com.numericalmethod.suanshu.matrix.doubles.factorization.triangle - package com.numericalmethod.suanshu.matrix.doubles.factorization.triangle
 
com.numericalmethod.suanshu.matrix.doubles.linearsystem - package com.numericalmethod.suanshu.matrix.doubles.linearsystem
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype - package com.numericalmethod.suanshu.matrix.doubles.matrixtype
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
 
com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary - package com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
 
com.numericalmethod.suanshu.matrix.doubles.operation - package com.numericalmethod.suanshu.matrix.doubles.operation
 
com.numericalmethod.suanshu.matrix.generic - package com.numericalmethod.suanshu.matrix.generic
 
com.numericalmethod.suanshu.matrix.generic.matrixtype - package com.numericalmethod.suanshu.matrix.generic.matrixtype
 
com.numericalmethod.suanshu.misc - package com.numericalmethod.suanshu.misc
 
com.numericalmethod.suanshu.number - package com.numericalmethod.suanshu.number
 
com.numericalmethod.suanshu.number.big - package com.numericalmethod.suanshu.number.big
 
com.numericalmethod.suanshu.number.complex - package com.numericalmethod.suanshu.number.complex
 
com.numericalmethod.suanshu.optimization - package com.numericalmethod.suanshu.optimization
 
com.numericalmethod.suanshu.optimization.constrained.general - package com.numericalmethod.suanshu.optimization.constrained.general
 
com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod - package com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
 
com.numericalmethod.suanshu.optimization.constrained.integer - package com.numericalmethod.suanshu.optimization.constrained.integer
 
com.numericalmethod.suanshu.optimization.constrained.linearprogramming - package com.numericalmethod.suanshu.optimization.constrained.linearprogramming
 
com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem - package com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem
 
com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex - package com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex
 
com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting - package com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting
 
com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard - package com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard
 
com.numericalmethod.suanshu.optimization.minmax - package com.numericalmethod.suanshu.optimization.minmax
 
com.numericalmethod.suanshu.optimization.unconstrained - package com.numericalmethod.suanshu.optimization.unconstrained
 
com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection - package com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
 
com.numericalmethod.suanshu.optimization.unconstrained.hessian - package com.numericalmethod.suanshu.optimization.unconstrained.hessian
 
com.numericalmethod.suanshu.optimization.unconstrained.linesearch - package com.numericalmethod.suanshu.optimization.unconstrained.linesearch
 
com.numericalmethod.suanshu.optimization.unconstrained.quasinewton - package com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
 
com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent - package com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent
 
com.numericalmethod.suanshu.optimization.univariate - package com.numericalmethod.suanshu.optimization.univariate
 
com.numericalmethod.suanshu.parallel - package com.numericalmethod.suanshu.parallel
 
com.numericalmethod.suanshu.signalprocessing - package com.numericalmethod.suanshu.signalprocessing
 
com.numericalmethod.suanshu.signalprocessing.filter - package com.numericalmethod.suanshu.signalprocessing.filter
 
com.numericalmethod.suanshu.stats.cointegration - package com.numericalmethod.suanshu.stats.cointegration
 
com.numericalmethod.suanshu.stats.descriptive - package com.numericalmethod.suanshu.stats.descriptive
 
com.numericalmethod.suanshu.stats.descriptive.moment - package com.numericalmethod.suanshu.stats.descriptive.moment
 
com.numericalmethod.suanshu.stats.descriptive.rank - package com.numericalmethod.suanshu.stats.descriptive.rank
 
com.numericalmethod.suanshu.stats.distribution - package com.numericalmethod.suanshu.stats.distribution
 
com.numericalmethod.suanshu.stats.distribution.univariate - package com.numericalmethod.suanshu.stats.distribution.univariate
 
com.numericalmethod.suanshu.stats.dlm - package com.numericalmethod.suanshu.stats.dlm
 
com.numericalmethod.suanshu.stats.hmm - package com.numericalmethod.suanshu.stats.hmm
 
com.numericalmethod.suanshu.stats.random - package com.numericalmethod.suanshu.stats.random
 
com.numericalmethod.suanshu.stats.random.distribution - package com.numericalmethod.suanshu.stats.random.distribution
 
com.numericalmethod.suanshu.stats.random.multivariate - package com.numericalmethod.suanshu.stats.random.multivariate
 
com.numericalmethod.suanshu.stats.random.pseudorandom - package com.numericalmethod.suanshu.stats.random.pseudorandom
 
com.numericalmethod.suanshu.stats.random.pseudorandom.linear - package com.numericalmethod.suanshu.stats.random.pseudorandom.linear
 
com.numericalmethod.suanshu.stats.regression.linear - package com.numericalmethod.suanshu.stats.regression.linear
 
com.numericalmethod.suanshu.stats.regression.linear.glm - package com.numericalmethod.suanshu.stats.regression.linear.glm
 
com.numericalmethod.suanshu.stats.regression.linear.glm.distribution - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
 
com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link - package com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
 
com.numericalmethod.suanshu.stats.regression.linear.glm.quasi - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
 
com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family - package com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
 
com.numericalmethod.suanshu.stats.regression.linear.logistic - package com.numericalmethod.suanshu.stats.regression.linear.logistic
 
com.numericalmethod.suanshu.stats.regression.linear.modelselection - package com.numericalmethod.suanshu.stats.regression.linear.modelselection
 
com.numericalmethod.suanshu.stats.regression.linear.ols - package com.numericalmethod.suanshu.stats.regression.linear.ols
 
com.numericalmethod.suanshu.stats.sampling.discrete - package com.numericalmethod.suanshu.stats.sampling.discrete
 
com.numericalmethod.suanshu.stats.stochasticprocess - package com.numericalmethod.suanshu.stats.stochasticprocess
 
com.numericalmethod.suanshu.stats.stochasticprocess.multivariate - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate
 
com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian
 
com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
 
com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
 
com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
 
com.numericalmethod.suanshu.stats.stochasticprocess.timepoints - package com.numericalmethod.suanshu.stats.stochasticprocess.timepoints
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
 
com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients - package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients
 
com.numericalmethod.suanshu.stats.test - package com.numericalmethod.suanshu.stats.test
 
com.numericalmethod.suanshu.stats.test.distribution - package com.numericalmethod.suanshu.stats.test.distribution
 
com.numericalmethod.suanshu.stats.test.distribution.kolmogorov - package com.numericalmethod.suanshu.stats.test.distribution.kolmogorov
 
com.numericalmethod.suanshu.stats.test.distribution.normality - package com.numericalmethod.suanshu.stats.test.distribution.normality
 
com.numericalmethod.suanshu.stats.test.distribution.pearson - package com.numericalmethod.suanshu.stats.test.distribution.pearson
 
com.numericalmethod.suanshu.stats.test.mean - package com.numericalmethod.suanshu.stats.test.mean
 
com.numericalmethod.suanshu.stats.test.rank - package com.numericalmethod.suanshu.stats.test.rank
 
com.numericalmethod.suanshu.stats.test.rank.wilcoxon - package com.numericalmethod.suanshu.stats.test.rank.wilcoxon
 
com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity - package com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
 
com.numericalmethod.suanshu.stats.test.timeseries.adf - package com.numericalmethod.suanshu.stats.test.timeseries.adf
 
com.numericalmethod.suanshu.stats.test.timeseries.portmanteau - package com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
 
com.numericalmethod.suanshu.stats.test.variance - package com.numericalmethod.suanshu.stats.test.variance
 
com.numericalmethod.suanshu.stats.timeseries - package com.numericalmethod.suanshu.stats.timeseries
 
com.numericalmethod.suanshu.stats.timeseries.linear.multivariate - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate
 
com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
 
com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima
 
com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma
 
com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch - package com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch
 
com.numericalmethod.suanshu.stats.timeseries.multivariate - package com.numericalmethod.suanshu.stats.timeseries.multivariate
 
com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime
 
com.numericalmethod.suanshu.stats.timeseries.univariate - package com.numericalmethod.suanshu.stats.timeseries.univariate
 
com.numericalmethod.suanshu.stats.timeseries.univariate.realtime - package com.numericalmethod.suanshu.stats.timeseries.univariate.realtime
 
com.numericalmethod.suanshu.time - package com.numericalmethod.suanshu.time
 
com.numericalmethod.suanshu.vector.doubles - package com.numericalmethod.suanshu.vector.doubles
 
com.numericalmethod.suanshu.vector.doubles.dense - package com.numericalmethod.suanshu.vector.doubles.dense
 
com.numericalmethod.suanshu.vector.doubles.dense.operation - package com.numericalmethod.suanshu.vector.doubles.dense.operation
 
Combination<T> - Class in com.numericalmethod.suanshu.algorithm
 
Combination(T[]...) - Constructor for class com.numericalmethod.suanshu.algorithm.Combination
 
combination(int, int) - Static method in class com.numericalmethod.suanshu.analysis.function.FunctionOps
Compute the combination function/binomial coefficient.
combination(int, int) - Static method in class com.numericalmethod.suanshu.number.big.BigIntegerUtils
Compute the combination function/binomial coefficient.
CombinedLinearCongruentialGenerator - Class in com.numericalmethod.suanshu.stats.random.pseudorandom.linear
This class takes a number of simple LinearCongruentialGenerator, such as Lehmer, to form one longer period generator by first summing values and then taking modulus.
CombinedLinearCongruentialGenerator(LinearCongruentialGenerator[]) - Constructor for class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.CombinedLinearCongruentialGenerator
Construct a linear congruential generator from some simpler and shorter modulus generators.
ComparableDateTime - Class in com.numericalmethod.suanshu.time
A wrapper around DateTime that implements the method public int compareTo(T o);.
ComparableDateTime(DateTime) - Constructor for class com.numericalmethod.suanshu.time.ComparableDateTime
Construct an instance from DateTime.
ComparableDateTime(int, int, int, int, int, int, int, DateTimeZone) - Constructor for class com.numericalmethod.suanshu.time.ComparableDateTime
 
compare(SparseElement, SparseElement) - Method in enum com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseElement.TopLeftFirstComparator
 
compare(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compare two BigDecimals up to a precision.
compare(Number, double) - Method in class com.numericalmethod.suanshu.number.complex.Complex
 
compare(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Compare two doubles.
compare(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Compare two doubles.
compare(Number, double) - Method in interface com.numericalmethod.suanshu.number.NumberUtils.Comparable
Compare this and that numbers up to a precision.
compare(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
Compare two Numbers.
compareTo(Real) - Method in class com.numericalmethod.suanshu.number.Real
 
compareTo(ComparableDateTime) - Method in class com.numericalmethod.suanshu.time.ComparableDateTime
 
complement() - Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
Get a copy of the basis of the orthogonal complement.
Complex - Class in com.numericalmethod.suanshu.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 com.numericalmethod.suanshu.number.complex.Complex
Construct a complex number from the real and imaginary parts.
Complex(double) - Constructor for class com.numericalmethod.suanshu.number.complex.Complex
Construct a complex number from a real number a.
ComplexMatrix - Class in com.numericalmethod.suanshu.matrix.generic.matrixtype
This class represents a matrix of Complex numbers.
ComplexMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
Construct a nRows x nCols matrix of Complex numbers.
ComplexMatrix(Complex[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
Construct a matrix from a 2D array of Complex numbers.
ComplexMatrix(double[][]) - Constructor for class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
Construct a ComplexMatrix from a 2D array of doubles.
complexRoots() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Roots
Get a copy of all the Complex but not real roots of the polynomial.
CompositeDoubleArrayOperation - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation
This class chooses which implementation to use based on the array length.
CompositeDoubleArrayOperation(int, DoubleArrayOperation, DoubleArrayOperation) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.CompositeDoubleArrayOperation
 
concat(double[]...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Concatenate arrays of doubles into one double[] array.
concat(Vector...) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.CreateVector
Take an array of vectors and concatenate them into one vector.
conditionalForEach(boolean, Iterable<T>, IterationBody<T>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Calls forEach only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Runs a parallel for-loop only if conditionToParallelize is true.
conditionalForLoop(boolean, int, int, LoopBody) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Calls conditionalForLoop with increment of 1.
ConditionalSumOfSquares - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma
This class does fitting for the ARIMA model by minimizing the conditional sum of squares (CSS).
ConditionalSumOfSquares(TimeSeries, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares
Fit an ARIMA model for the observations.
confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.mean.T
Compute the confidence interval.
confidenceInterval(double) - Method in class com.numericalmethod.suanshu.stats.test.variance.F
Compute the confidence interval.
conjugate() - Method in class com.numericalmethod.suanshu.number.complex.Complex
Conjugate of (a + bi) is (a - bi).
ConjugateGradient - Class in com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection
Conjugate-gradient method generates a new search direction by adding a vector βkdk to the negative gradient -gk+1.
ConjugateGradient() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.conjugatedirection.ConjugateGradient
 
ConjugateGradientNormalErrorSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient method (CG) can be applied on the normal equations.
ConjugateGradientNormalErrorSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
 
ConjugateGradientNormalErrorSolver(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalErrorSolver
The solver recomputes the residual as b - Axi once per this number of iterations
ConjugateGradientNormalResidualSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient method (CG) can be applied on the normal equations.
ConjugateGradientNormalResidualSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
 
ConjugateGradientNormalResidualSolver(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientNormalResidualSolver
The solver recomputes the residual as b - Axi once per this number of iterations
ConjugateGradientSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient method (CG) is useful for solving symmetric n-by-n linear systems.
ConjugateGradientSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
 
ConjugateGradientSolver(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSolver
The solver recomputes the residual as b - Axi once per this number of iterations
ConjugateGradientSquaredSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
The Conjugate Gradient Squared method (CGS) is useful for solving non-symmetric n-by-n linear systems.
ConjugateGradientSquaredSolver() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
 
ConjugateGradientSquaredSolver(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.ConjugateGradientSquaredSolver
The solver recomputes the residual as b - Axi once per this number of iterations
Constant - Class in com.numericalmethod.suanshu
This class lists the global parameters and constants in this library.
ConstantDrift - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
The class represents a constant drift function.
ConstantDrift(Vector) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDrift
Construct a constant drift function.
ConstantSigma1 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
The class represents a constant diffusion coefficient function.
ConstantSigma1(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
Construct a constant diffusion coefficient function.
ConstantSigma2 - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients
Deprecated. This implementation is slow. Use ConstantSigma1 instead.
ConstantSigma2(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2
Deprecated. Construct a constant diffusion coefficient function.
ConstrainedMinimizer - Interface in com.numericalmethod.suanshu.optimization.constrained.general
 
ConstrainedMinimizerFactory - Interface in com.numericalmethod.suanshu.optimization.constrained.general
 
ConstrainedProblem - Class in com.numericalmethod.suanshu.optimization.constrained.general
This class defines a constrained optimization problem in this form.
ConstrainedProblem(RealScalarFunction, EqualityConstraints, InequalityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.ConstrainedProblem
Construct a constrained optimization problem.
Constraints - Class in com.numericalmethod.suanshu.optimization.constrained.general
This class defines a set of constraints for a constrained optimization problem.
Constraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.Constraints
Construct a constraint set for a constrained optimization problem.
constraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.Constraints
Get the set of constraints.
Construction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
This interface defines how to construct a realization for a stochastic process.
Construction - Interface in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
This interface defines how a realization of a stochastic process is constructed.
ContinuedFraction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
A continued fraction representation of a number.
ContinuedFraction(ContinuedFraction.Partials) - Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction
Construct an instance of a continued fraction.
ContinuedFraction.MaxIterationsExceededException - Exception in com.numericalmethod.suanshu.analysis.function.rn2r1
RuntimeException thrown when the continued fraction fails to converge for a given epsilon before a certain number of iterations.
ContinuedFraction.MaxIterationsExceededException(String) - Constructor for exception com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction.MaxIterationsExceededException
 
ContinuedFraction.Partials - Interface in com.numericalmethod.suanshu.analysis.function.rn2r1
Definition of a continued fraction in terms of the partial numerators an, and the partial denominators bn.
cookDistances - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Diagnostics
Cook distance
Coordinates - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
This class represents the coordinates (i, j) of a matrix element, where i and j are the row-index and column-index of the element, respectively.
Coordinates(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
Construct a matrix coordinate specifying an entry location.
coordinates - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseElement
the coordinates of this element
copyAndReplace(Matrix, int, int, int, int, Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.CreateMatrix
Deprecated. Not supported yet.
correlation() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance
Get the correlation.
correlationMatrix() - Method in class com.numericalmethod.suanshu.stats.descriptive.CovarianceMatrix
Get a copy of the correlation matrix.
cos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Cosine of a complex number (a + bi).
cosh(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Hyperbolic cosine of a complex number (a + bi).
count() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.CountMonitor
Get the number of iterates generated by the iterative algorithm.
count(double) - Method in class com.numericalmethod.suanshu.number.Counter
Get the counts, i.e., the number of repetitions, for a particular double.
Counter - Class in com.numericalmethod.suanshu.number
This class counts the number of repeated numbers.
Counter() - Constructor for class com.numericalmethod.suanshu.number.Counter
Construct an instance of Counter.
Counter(int) - Constructor for class com.numericalmethod.suanshu.number.Counter
Construct an instance of Counter.
CountMonitor - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
This simple IterationMonitor monitors the number of iterates generated, hence iterations.
CountMonitor() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.CountMonitor
 
Courant - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod
This penalty function sums up the squared error penalties.
Courant(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
Construct a Courant penalty function from a set of equality constraints.
Courant(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
Construct a Courant penalty function from a set of equality constraints.
Courant(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
Construct a Courant penalty function from a set of equality constraints.
Covariance - Class in com.numericalmethod.suanshu.stats.descriptive
Covariance is a measure of how much two variables change together.
Covariance() - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
Construct an empty Covariance calculator.
Covariance(double[][]) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
Construct a Covariance calculator, initialized with two samples.
Covariance(Covariance) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.Covariance
Copy constructor.
covariance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
the covariance matrix of the coefficient estimates, β^
covariance(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
Get the covariance matrix for prediction errors at time t for X^t+1.
covariance() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaFitting
Get the asymptotic covariance matrix of the estimators.
covariance() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares
Compute the asymptotic covariance matrix for the estimated parameters, φ and θ.
CovarianceMatrix - Class in com.numericalmethod.suanshu.stats.descriptive
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.
CovarianceMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.stats.descriptive.CovarianceMatrix
Construct the covariance matrix for a matrix.
CreateMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.operation
This class collects the utility functions to create a new matrix/vector from existing ones.
CreateVector - Class in com.numericalmethod.suanshu.vector.doubles.dense.operation
CreateVector a new vector from an existing vector.
CsrSparseMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse
The compressed sparse row (CSR) format for sparse matrix.
CsrSparseMatrix(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
Create an instance of CSR sparse matrix with the matrix dimension.
CsrSparseMatrix(int, int, int[], int[], double[]) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
Create an instance of CSR sparse matrix with non-zero values.
CsrSparseMatrix(int, int, List<SparseElement>) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
Create an instance of CSR sparse matrix with a list of non-zero SparseElements.
CsrSparseMatrix(CsrSparseMatrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
Copy constructor.
Ctor2x2(double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct GivensMatrix(2, 1, 2, c, s)
CtorFromRho(int, int, int, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct a Givens matrix from ρ.
CtorToRotateColumns(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct a Givens matrix such that [a b] %*% G = [* 0].
CtorToRotateRows(int, int, int, double, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct a Givens matrix such that G %*% [a b]transpose = [* 0]transpose.
CtorToZeroOutEntry(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct a Givens matrix such that G %*% A has 0 in the A[i, j] entry.
CtorToZeroOutEntryByTranspose(Matrix, int, int) - Static method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.GivensMatrix
Construct a Givens matrix such that Gtranspose %*% A has 0 in the A[i, j] entry.
Cubic - Class in com.numericalmethod.suanshu.analysis.function.polynomial.root
This is a cubic equation solver.
Cubic() - Constructor for class com.numericalmethod.suanshu.analysis.function.polynomial.root.Cubic
 
cumsum(double[]) - Static method in class com.numericalmethod.suanshu.misc.R
Get an array whose elements are the cumulative sum of the elements of the input array.
cumsum(int[]) - Static method in class com.numericalmethod.suanshu.misc.R
Get an array whose elements are the cumulative sum of the elements of the input array.
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
 
cumulant(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution
The cumulant function of the exponential distribution.
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
 
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
 
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
 
cumulant(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
 
CumulativeNormal - Class in com.numericalmethod.suanshu.analysis.function.special
This computes an approximation to the cumulative Normal distribution function, N(x).
CumulativeNormal(CumulativeNormal.Method) - Constructor for class com.numericalmethod.suanshu.analysis.function.special.CumulativeNormal
Construct an instance to compute the cumulative Normal function.
CumulativeNormal() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.CumulativeNormal
Construct an instance to compute cumulative Normal function.
CumulativeNormal.Method - Enum in com.numericalmethod.suanshu.analysis.function.special
the methods available to compute the cumulative Normal
CumulativeNormalInverse - Class in com.numericalmethod.suanshu.analysis.function.special
This computes an approximation to the quantile function of the cumulative Normal distribution function, N-1(x) We use the Beasley-Springer-Moro algorithm.
CumulativeNormalInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.CumulativeNormalInverse
 

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