A-Index (SuanShu 1.3.1 API Documentation)

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SuanShu, a Java numerical and statistical library

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A

a() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.QuadraticSyntheticDivision: Get a as in the remainder (b * (x + u) + a).
A(int, double) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction.Partials: Compute a_n.
a - Variable in class com.numericalmethod.suanshu.analysis.function.special.Gaussian: a as in f(x) = a * exp{-(x - b)² / 2 / c²}
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.DoubleExponential: the lower limit
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential: the lower limit
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable: the lower limit
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable: the lower limit
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.PowerLawSingularity: the lower limit
a - Variable in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.StandardInterval: the lower limit
A - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver: matrix A as in Ax = b
A() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem: Get the coefficient matrix of the problem.
A - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem1: A as in A * x ≤ b
A - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.problem.StandardLpProblem2: A as in A * x ≥ b
A() - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel: Get the state transition probabilities.
a - Variable in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer: the multiplier
A - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem: the design matrix, the regressors, including the intercept if any; each column corresponds to one regressor a n x m matrix
A - Variable in class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix: a random matrix constructed
a0() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchModel: Get the constant term.
AbelianGroup<G> - Interface in com.numericalmethod.suanshu.mathstructure: This interface represents an Abelian group.
abs(double[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Get the absolute values.
absoluteError(double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Compute the absolute difference between x1 and x0.
AbsoluteError - Class in com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod: This penalty function sums up the absolute error penalties.
AbsoluteError(EqualityConstraints, double[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError: Construct an AbsoluteError penalty function from a set of equality constraints.
AbsoluteError(EqualityConstraints, double) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError: Construct an AbsoluteError penalty function from a set of equality constraints.
AbsoluteError(EqualityConstraints) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError: Construct an AbsoluteError penalty function from a set of equality constraints.
AbsoluteTolerance - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative: The stopping criteria is that the norm of the residual r is equal to or smaller than the specified tolerance, that is, ||r||₂ ≤ tolerance
AbsoluteTolerance() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.AbsoluteTolerance: Create an instance which uses AbsoluteTolerance.DEFAULT_TOLERANCE.
AbsoluteTolerance(double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.AbsoluteTolerance: Create an instance which uses the specified tolerance.
acos(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction: Inverse of cosine.
add(Polynomial) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
add(Number...) - Method in class com.numericalmethod.suanshu.datastructure.list.NumberList: Add new numbers to the list.
add(String...) - Method in class com.numericalmethod.suanshu.datastructure.list.NumberList: Add new numbers to the list.
add(Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Intervals: Add an Interval to the set.
add(Interval<T>...) - Method in class com.numericalmethod.suanshu.interval.Intervals: Add a collection of Intervals to the set.
add(G) - Method in interface com.numericalmethod.suanshu.mathstructure.AbelianGroup: + : G × G → G
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
add(Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.MatrixRing: this + that
add(DenseData) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData: Add up the elements in this and that, element-by-element.
add(DenseMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseMatrix
add(DiagonalMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.DiagonalMatrix: this + that Add with a DiagonalMatrix.
add(TridiagonalMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.TridiagonalMatrix
add(LowerTriangularMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.LowerTriangularMatrix: this + that Add with a LowerTriangularMatrix.
add(SymmetricMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
add(UpperTriangularMatrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.UpperTriangularMatrix
add(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.CompositeDoubleArrayOperation
add(double[], double[]) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.DoubleArrayOperation
add(Matrix, Matrix) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.MatrixMathOperation: A1 + A2
add(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.ParallelDoubleArrayOperation
add(double[], double[]) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleDoubleArrayOperation
add(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.mathoperation.SimpleMatrixMathOperation: A1 + A2.
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix
add(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
add(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct
add(Matrix) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef
add(ComplexMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
add(GenericMatrix<F>) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
add(RealMatrix) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
add(Complex) - Method in class com.numericalmethod.suanshu.number.complex.Complex
add(double) - Method in class com.numericalmethod.suanshu.number.Counter: Add a new double to the counter.
add(double...) - Method in class com.numericalmethod.suanshu.number.Counter: Add more doubles to the counter.
add(Real) - Method in class com.numericalmethod.suanshu.number.Real
add(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
add(DenseVector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector: this + that
add(double) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
add(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
add(double) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
add(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector: this + that
add(double) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector: v + s Add a scalar to all entries in the vector v.
addColumn(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation: Column addition: A[col1, ] = A[col1, ] + scale * A[col2, ]
addConstraint(RealScalarFunction) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.Constraints: Add a constraint to the set.
addData(double[], double[]) - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable: Add more data points for interpolation.
addData(double[], double[]) - Method in interface com.numericalmethod.suanshu.analysis.interpolation.UnivariateRealInterpolator: Supply the interpolator with a set of data points.
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance: Recompute the statistic, incrementally if possible.
addData(double[][]) - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance: Update the covariance statistic with more data.
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile
addData(double...) - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic: Recompute the statistic, incrementally if possible.
addData(double...) - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic
addIntercept - Variable in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem: true iff to add an intercept term to the linear regression
addIterate(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.CountMonitor
addIterate(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IteratesMonitor
addIterate(Vector) - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterationMonitor: Add a newly computed iterate to this monitor.
addIterate(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.NullMonitor
AdditiveModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess: The additive model of a time series is an additive composite of the trend, seasonality and irregular random components.
AdditiveModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel: Construct a univariate time series by adding up the components.
AdditiveModel(double[], double[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.AdditiveModel: Construct a univariate time series by adding up the components.
addRow(double, double[]) - Method in class com.numericalmethod.suanshu.datastructure.MathTable: Add a row to the table.
addRow(int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation: Row addition: A[row1, ] = A[row1, ] + scale * A[row2, ]
addRows(double[][]) - Method in class com.numericalmethod.suanshu.datastructure.MathTable: Add rows by a 2D double array, double[][].
AdfAsymptoticDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf: This class computes the asymptotic distribution of the augmented Dickey-Fuller (ADF) test statistics.
AdfAsymptoticDistribution(AugmentedDickeyFuller.TrendType, int, int, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution: Construct the asymptotic distribution for the augmented Dickey-Fuller test statistics.
AdfAsymptoticDistribution(AugmentedDickeyFuller.TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution: Construct the asymptotic distribution for the augmented Dickey-Fuller test statistics.
AdfAsymptoticDistribution1 - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf: This is the asymptotic distribution of the Augmented Dickey-Fuller test statistics, for the CONSTANT_TIME case.
AdfAsymptoticDistribution1(AdfAsymptoticDistribution1.Type) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution1: Construct the asymptotic distribution for the Augmented Dickey Fuller test statistics.
AdfAsymptoticDistribution1(int, int, AdfAsymptoticDistribution1.Type, long) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution1: Construct the asymptotic distribution for the Augmented Dickey Fuller test statistics.
AdfAsymptoticDistribution1.Type - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf: the types of Dickey-Fuller tests available
AdfDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf
AdfFiniteSampleDistribution - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf: This class computes the finite sample distribution of the augmented Dickey-Fuller (ADF) test statistics.
AdfFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType, boolean, int, int, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution: Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
AdfFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType, boolean, int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution: Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
AdfFiniteSampleDistribution(int, AugmentedDickeyFuller.TrendType) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution: Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
AdfFiniteSampleDistribution(int) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution: Construct the finite sample distribution for the augmented Dickey-Fuller test statistics.
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
AIC(Vector, Vector, Vector, double, double, int) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.ExponentialDistribution: AIC = 2 * #param - 2 * log-likelihood
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gamma
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Gaussian
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
AIC(Vector, Vector, Vector, double, double, int) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Poisson
AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic: the AIC
AIC - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.InformationCriteria: Akaike information criterion
AIC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaFitting: Compute the AIC of model fitting.
AIC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares: Compute the AIC, a model selection criterion.
AICC() - Method in interface com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaFitting: Compute the AICC of model fitting.
AICC() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares: Compute the AICC, a model selection criterion.
ak - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl
Algebra - Interface in com.numericalmethod.suanshu.interval: Allen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F.
Algebra.Relation - Enum in com.numericalmethod.suanshu.interval
algebraicMultiplicity() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Property: Get the multiplicity of the eigenvalue as a root of the characteristic polynomial, aka the algebraic multiplicity.
allRoots() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.root.Roots: Get a copy of all the roots of the polynomial.
allZeros(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Check if a double[] array consists of all 0s, entry-by-entry.
alpha - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead: the reflection coefficient
alpha() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle: Get the set of adjusting coefficients.
alpha - Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution: α: the shape parameter
alpha() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchModel: Get the ARCH coefficients.
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest: Get a description of the alternative hypothesis.
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F
alternativeHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene
angle(H) - Method in interface com.numericalmethod.suanshu.mathstructure.HilbertSpace: angle : H × H → F Inner products formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
angle(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
angle(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
angle(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector: Measure the angle between this and that.
anyZero(double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Check if a double[] array has any 0.
AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the i-th AR coefficient; AR(0) = 1.
AR() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Get the AR coefficients, excluding the initial 1.
AR - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: the AR coefficients
AR(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Get the i-th AR coefficient; AR(0) = 1.
AR() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Get the AR coefficients, excluding the initial 1.
AR2 - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.Residuals: diagnostic measure: the adjusted R-squared
AreMatrices - Class in com.numericalmethod.suanshu.matrix.doubles: This class collects the boolean operators that take two or more matrices or vectors and compare their properties.
arg() - Method in class com.numericalmethod.suanshu.number.complex.Complex: Get the θ of the complex number's polar representation.
ArimaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima: This class represents a multivariate ARIMA model.
ArimaModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Construct a multivariate ARIMA model.
ArimaModel(Vector, Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Construct a multivariate ARIMA model with unit variance.
ArimaModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Construct a zero-intercept (mu) multivariate ARIMA model.
ArimaModel(Matrix[], int, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Construct a zero-intercept (mu) multivariate ARIMA model with unit variance.
ArimaModel(ArimaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Copy constructor.
ArimaModel(ArimaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaModel: Cast a univariate ARIMA model to a multivariate model.
ArimaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima: This class represents an ARIMA model.
ArimaModel(double, double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaModel: Construct a univariate ARIMA model.
ArimaModel(double, double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaModel: Construct a univariate ARIMA model with unit variance.
ArimaModel(double[], int, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaModel: Construct a zero-intercept (mu) univariate ARIMA model.
ArimaModel(double[], int, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaModel: Construct a zero-intercept (mu) univariate ARIMA model with unit variance.
ArimaModel(ArimaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaModel: Copy constructor.
ArimaSim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima: This class generates simulations of a multivariate ARIMA model.
ArimaSim(int, ArimaModel, long) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaSim: Simulate a multivariate ARIMA process.
ArimaSim(int, ArimaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaSim: Simulate a multivariate ARIMA process.
ArimaSim - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima: This class simulates the ARIMA models.
ArimaSim(int, ArimaModel, Innovations) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaSim: Simulate an ARIMA model.
ArimaSim(int, ArimaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaSim: Simulate an ARIMA model.
ArimaxModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima: This class represents a multivariate ARIMAX (ARIMA model with eXogenous inputs) model.
ArimaxModel(Vector, Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Construct a multivariate ARIMAX (ARIMA model with eXogenous inputs) model.
ArimaxModel(Vector, Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Construct a multivariate ARIMAX model with unit variance.
ArimaxModel(Matrix[], int, Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Construct a zero-intercept (mu) multivariate ARIMAX model.
ArimaxModel(Matrix[], int, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Construct a zero-intercept (mu) multivariate ARIMAX model with unit variance.
ArimaxModel(ArimaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Copy constructor.
ArimaxModel(ArimaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.ArimaxModel: Cast a univariate ARIMAX model to a multivariate model.
ArimaxModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima: This class represents a univariate ARIMAX (ARIMA model with eXogenous inputs) model.
ArimaxModel(double, double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Construct a univariate ARIMAX (ARIMA model with eXogenous inputs) model.
ArimaxModel(double, double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Construct a univariate ARIMAX model with unit variance.
ArimaxModel(double[], int, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Construct a zero-intercept (mu) univariate ARIMAX model.
ArimaxModel(double[], int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Construct a zero-intercept (mu) univariate ARIMAX model with unit variance.
ArimaxModel(ArimaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.ArimaxModel: Copy constructor.
ArmaFitting - Interface in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: This interface represents a fitting method for estimating φ, θ, μ and σ^2 in an ARMA model.
armaMean(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Compute the multivariate ARMA conditional mean.
armaMean(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Compute the univariate ARMA conditional mean.
armaMeanNoIntercept(Matrix, Matrix) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Compute the zero-intercept (mu) multivariate ARMA conditional mean.
armaMeanNoIntercept(double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Compute the zero-intercept (mu) univariate ARMA conditional mean.
ArmaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: This class represents a multivariate ARMA model.
ArmaModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Construct a multivariate ARMA model.
ArmaModel(Vector, Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Construct a multivariate ARMA model with unit variance.
ArmaModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Construct a zero-intercept (mu) multivariate ARMA model.
ArmaModel(Matrix[], Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Construct a zero-intercept (mu) multivariate ARMA model with unit variance.
ArmaModel(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Copy constructor.
ArmaModel(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaModel: Cast a univariate ARMA model to a multivariate model.
ArmaModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: This class represents a univariate ARMA model.
ArmaModel(double, double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Construct a univariate ARMA model.
ArmaModel(double, double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Construct a univariate ARMA model with unit variance.
ArmaModel(double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Construct a zero-intercept (mu) univariate ARMA model.
ArmaModel(double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Construct a zero-intercept (mu) univariate ARMA model with unit variance.
ArmaModel(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaModel: Copy constructor.
armaxMean(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Compute the multivariate ARMAX conditional mean.
armaxMean(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Compute the univariate ARMAX conditional mean.
armaxMeanNoIntercept(Matrix, Matrix, Vector) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Compute the zero-intercept (mu) multivariate ARMAX conditional mean.
armaxMeanNoIntercept(double[], double[], double[]) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Compute the zero-intercept (mu) univariate ARMAX conditional mean.
ArmaxModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: This class represents a multivariate ARMAX (ARMA model with eXogenous inputs) model.
ArmaxModel(Vector, Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Construct a multivariate ARMAX (ARMA model with eXogenous inputs) model.
ArmaxModel(Vector, Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Construct a multivariate ARMAX model with unit variance.
ArmaxModel(Matrix[], Matrix[], Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Construct a zero-intercept (mu) multivariate ARMAX model.
ArmaxModel(Matrix[], Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Construct a zero-intercept (mu) multivariate ARMAX model with unit variance.
ArmaxModel(ArmaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Copy constructor.
ArmaxModel(ArmaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArmaxModel: Cast a univariate ARMAX model to a multivariate model.
ArmaxModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: This class represents a univariate ARMAX (ARMA model with eXogenous inputs) model.
ArmaxModel(double, double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Construct a univariate ARMAX (ARMA model with eXogenous inputs) model.
ArmaxModel(double, double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Construct a univariate ARMAX model with unit variance.
ArmaxModel(double[], double[], double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Construct a zero-intercept (mu) univariate ARMAX model.
ArmaxModel(double[], double[], double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Construct a zero-intercept (mu) univariate ARMAX model with unit variance.
ArmaxModel(ArmaxModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArmaxModel: Copy constructor.
ArModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: This class represents a VAR model.
ArModel(Vector, Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Construct a VAR model.
ArModel(Vector, Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Construct a VAR model with unit variance.
ArModel(Matrix[], Matrix) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Construct a zero-intercept (mu) VAR model.
ArModel(Matrix[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Construct a zero-intercept (mu) VAR model with unit variance.
ArModel(ArModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Copy constructor.
ArModel(ArModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.ArModel: Cast a univariate AR model to a multivariate model.
ArModel - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: This class represents an AR model.
ArModel(double, double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArModel: Construct a univariate AR model.
ArModel(double, double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArModel: Construct a univariate AR model with unit variance.
ArModel(double[], double) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArModel: Construct a zero-intercept (mu) univariate AR model.
ArModel(double[]) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArModel: Construct a zero-intercept (mu) univariate AR model with unit variance.
ArModel(ArModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ArModel: Copy constructor.
ArrayList2doubleArray(ArrayList<Double>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Convert an ArrayList to a double[] array.
ArrayList2intArray(ArrayList<Integer>) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils: Convert an ArrayList to a int[] array.
as(Vector) - Static method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector: Convert a vector to type DenseVector.
AS159 - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson: Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums.
AS159(int[], int[]) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159: Construct a random table generator according to row and column totals.
AS159(int[], int[], RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159: Construct a random table generator according to row and column totals.
AS159.RandomMatrix - Class in com.numericalmethod.suanshu.stats.test.distribution.pearson: a random matrix generated by AS159 and its probability
AS159.RandomMatrix(Matrix, double) - Constructor for class com.numericalmethod.suanshu.stats.test.distribution.pearson.AS159.RandomMatrix
asDateTime() - Method in class com.numericalmethod.suanshu.time.ComparableDateTime: Cast the instance to DateTime.
asFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
asFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gamma
asFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Gaussian
asFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
asFamily() - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Poisson
asFamily() - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.QuasiFamily
asin(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction: Inverse of sine.
assertArgument(boolean, String) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils: Check if an argument condition is satisfied.
assertOrThrow(RuntimeException) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils: Throw the argument RuntimeException if it is not null.
asymptoticCDF(double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution: the asymptotic distribution of the KolmogorovDistribution distribution
asymptoticCDF(double, double) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution: the asymptotic distribution of the one-sided Kolmogorov distribution
atan(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction: Inverse of tangent.
AugmentedDickeyFuller - Class in com.numericalmethod.suanshu.stats.test.timeseries.adf: The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time series stationary.
AugmentedDickeyFuller(double[], AugmentedDickeyFuller.TrendType, int, AdfDistribution) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller: Perform the Augmented Dickey-Fuller test statistics to test for the existence of uniroot.
AugmentedDickeyFuller(double[]) - Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller: Perform the Augmented Dickey-Fuller test statistics to test for the existence of uniroot.
AugmentedDickeyFuller.TrendType - Enum in com.numericalmethod.suanshu.stats.test.timeseries.adf: the three versions of augmented Dickey-Fuller (ADF) test
AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.
AutoCorrelation(ArimaModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCorrelation: Compute the auto-correlation function of a vector ARMA model.
AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample: This computes the sample Auto-Correlation Function (ACF) for a univariate data set.
AutoCorrelation(TimeSeries, AutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
AutoCorrelation(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
AutoCorrelation - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.
AutoCorrelation(ArimaModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCorrelation: Compute the auto-correlation function of an ARMA model.
AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate: This class represents an auto-correlation function for a multi-dimensional time series {Xt}.
AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCorrelationFunction
AutoCorrelationFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate: This class represents an auto-correlation function for a univariate time series {x_t}, For stationary process, the auto-correlation depends only on the lag, |i - j|.
AutoCorrelationFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCorrelationFunction
AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma: Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.
AutoCovariance(ArimaModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCovariance: Compute the auto-covariance function of a vector ARMA model.
AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample: This computes the sample Auto-Covariance Function (ACVF) for a univariate data set.
AutoCovariance(TimeSeries, AutoCovariance.Type) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
AutoCovariance(TimeSeries) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
AutoCovariance - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma: Compute the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model, assuming that EX_t = 0.
AutoCovariance(ArimaModel, double, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCovariance: Compute the auto-covariance function of an ARMA model.
AutoCovariance.Type - Enum in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample
AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate: This class represents an auto-covariance function for a multi-dimensional time series {Xt}, K(i, j) = E((Xi - μi) * (Xj - μj)') For stationary process, the auto-covariance depends only on the lag, |i - j|.
AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.AutoCovarianceFunction
AutoCovarianceFunction - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate: This class represents an auto-covariance function for a univariate time series {x_t}, For stationary process, the auto-covariance depends only on the lag, |i - j|.
AutoCovarianceFunction() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction
autoEpsilon(double...) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils: Compute a reasonable precision parameter.
autoEpsilon(double[]...) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils: Compute a reasonable precision parameter.
autoEpsilon(Matrix) - Static method in class com.numericalmethod.suanshu.misc.SuanShuUtils: Compute a reasonable precision parameter.
auxiliaryOlsRegression(Vector, Residuals) - Method in class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.White: the auxiliary regression