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

N

N() - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable

Get the number of data points.

n - Variable in class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.QuasiNewton.QuasiNewtonImpl

number of variables

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.Covariance

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Kurtosis

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Mean

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Moments

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Skewness

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.moment.Variance

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Max

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Min

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.rank.Quantile

N() - Method in interface com.numericalmethod.suanshu.stats.descriptive.Statistic

Get the size of the sample.

N() - Method in class com.numericalmethod.suanshu.stats.descriptive.SynchronizedStatistic

N - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid

the number of time points

N - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.UnitGrid

the number of time points

n - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation

the number of discretization in the integral time interval

n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution

the number of observations

n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution

the number of observations

n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

the total number of observations of the two samples

N - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBeraDistribution

the number of observations in a sample

n - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution

the number of observations

N - Variable in class com.numericalmethod.suanshu.stats.test.HypothesisTest

total number of observations

N - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution

number of observations in group 2

N - Variable in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution

number of observations in group 2

n1 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

the number of observations of the first sample

n2 - Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution

the number of observations of the second sample

N_POINTS_4_EXTRAPOLATION - Static variable in class com.numericalmethod.suanshu.analysis.differentiation.Ridders

the default number of points for extrapolation

NaiveRule - Class in com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting

This is the rule found on p. 49.

NaiveRule() - Constructor for class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.pivoting.NaiveRule

name - Variable in class com.numericalmethod.suanshu.license.Package

NaN - Static variable in class com.numericalmethod.suanshu.number.complex.Complex

a number representing a Not-a-Number (NaN) value of type Complex

nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian

nB() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.DiscretizedSDE

Get the number of independent driving Brownian motions.

nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler

nB() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft

Get the number of independent Brownian motions.

nB - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.SDE

number of independent driving Brownian motions

ncols - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination

number of columns

ncols - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt

number of columns

ncols - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection

number of columns

nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix

ncols - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel

number of columns

nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl

nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct

nCols() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef

nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix

nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix

nCols() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix

nCols() - Method in interface com.numericalmethod.suanshu.matrix.MatrixDimension

Get the number of columns.

nCols() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau

Get the number of non-basic variables.

ncols() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1

ncols() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2

Deprecated.

ncols() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion

Get the number of independent Brownian motions.

nColumns() - Method in class com.numericalmethod.suanshu.datastructure.MathTable

Get the number of columns in the table.

nConstraints() - Method in class com.numericalmethod.suanshu.optimization.constrained.general.Constraints

Get the number of constraints.

NEGATIVE_INFINITY - Static variable in class com.numericalmethod.suanshu.number.complex.Complex

a number representing the negative infinity of type Complex

NelderMead - Class in com.numericalmethod.suanshu.optimization.unconstrained

The Nelder–Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems.

NelderMead(double, double, double, double) - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead

Construct a NelderMead instance to minimize/maximize f with the control parameters.

NelderMead() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.NelderMead

Construct a NelderMead instance to minimize an objective function with the default control parameters.

NelderMead.BuildSimplex - Interface in com.numericalmethod.suanshu.optimization.unconstrained

The Nelder–Mead algorithm requires an initial simplex to start the search.

NevilleTable - Class in com.numericalmethod.suanshu.analysis.interpolation

Neville's algorithm is an algorithm for polynomial interpolation.

NevilleTable(int, double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable

Construct an instance of a Neville Table with size n, initialized with data {(x, y)}.

NevilleTable(double[], double[]) - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable

Construct an instance of a Neville Table, initialized with data {(x, y)}.

NevilleTable() - Constructor for class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable

Construct an empty Neville Table.

NevilleTable.DuplicatedAbscissae - Exception in com.numericalmethod.suanshu.analysis.interpolation

RuntimeException thrown when there are duplicated abscissae.

NevilleTable.DuplicatedAbscissae(String) - Constructor for exception com.numericalmethod.suanshu.analysis.interpolation.NevilleTable.DuplicatedAbscissae

newInstance() - Method in interface com.numericalmethod.suanshu.optimization.constrained.general.ConstrainedMinimizerFactory

Newton - Class in com.numericalmethod.suanshu.analysis.uniroot

Newton–Raphson method is an iterative root finding method for univariate functions.

Newton(UnivariateRealFunction, UnivariateRealFunction, double) - Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Newton

Construct an instance of Newton's root finding algorithm.

Newton(UnivariateRealFunction, double) - Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Newton

Construct an instance of Newton's root finding algorithm.

NewtonRaphson - Class in com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent

The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series.

NewtonRaphson() - Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.steepestdescent.NewtonRaphson

nExogenousFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the number of factors, excluding the intercept

next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.EulerMaclaurin

next(int, UnivariateRealFunction, double, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.IterativeIntegrator

Compute a refined sum for the integral.

next(int, UnivariateRealFunction, double, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson

next() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.TimeSeries.Iterator

Get the next entry in the time series, if any.

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries.Iterator

next() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries.Iterator

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.distribution.Gaussian

Get the next Gaussian sample.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.distribution.InverseTransformSampling

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.distribution.StandardGaussian

This function is more efficient, saving two floating point computations.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.distribution.Uniform

Get the next random double between 0 and 1.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.CombinedLinearCongruentialGenerator

Get the next random double between 0 and 1.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer

Get the next random double between 0 and 1.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer

Get the next random double between 0 and 1.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.MRG

Get the next random double between 0 and 1.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.MersenneTwister

nextDouble() - Method in interface com.numericalmethod.suanshu.stats.random.RandomNumberGenerator

Get the next random double.

nextDouble() - Method in class com.numericalmethod.suanshu.stats.random.RNG

Get a double between 0 and 1.

nextId() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.PathByIdImpl

Generate an ID for this particular path.

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.distribution.Uniform

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.CombinedLinearCongruentialGenerator

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.LEcuyer

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.Lehmer

All built-in linear random number generators in this library ultimately call this function to generate random numbers.

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.linear.MRG

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.pseudorandom.MersenneTwister

Get a positive long random number.

nextLong() - Method in interface com.numericalmethod.suanshu.stats.random.RandomLongGenerator

Get the next random long.

nextLong() - Method in class com.numericalmethod.suanshu.stats.random.RNG

nextValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries.Iterator

Get the next value in the time series.

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries.Iterator

nextValue() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries.Iterator

Get the next value in the time series.

nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.Gaussian

nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.IID

nextVector() - Method in class com.numericalmethod.suanshu.stats.random.multivariate.Multinomial

nextVector() - Method in interface com.numericalmethod.suanshu.stats.random.multivariate.RandomVectorGenerator

Get the next random vector.

nextWeekDay(DateTime) - Static method in class com.numericalmethod.suanshu.time.JodaTimeUtils

Get the next weekday, i.e., skipping Saturdays and Sundays.

nFactors() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the number of factors, including the intercept if any

nLags - Variable in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCovariance

the number of lags in the result

nnz() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.CsrSparseMatrix

nnz() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.DokSparseMatrix

nnz() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.LilSparseMatrix

nnz() - Method in interface com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseStructure

Get the number of non-zero entries in the matrix.

nnz() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector

no(double) - Method in interface com.numericalmethod.suanshu.misc.R.ifelse

the value to return if x does not satisfy the if part of the statement

nObs() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem

the number of observations

NoChangeOfVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution

This is a dummy substitution rule that does not change any variable.

NoChangeOfVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.NoChangeOfVariable

Construct an instance of the NoChangeOfVariable substitution rule.

norm() - Method in interface com.numericalmethod.suanshu.mathstructure.BanachSpace

||·|| : B → F norm is a function that assigns a strictly positive length or size to all vectors in a vector space, other than the zero vector.

norm() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector

norm(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector

norm(int) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector

norm() - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector

norm(int) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector

norm() - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector

Compute the length or magnitude or Euclidean norm of a vector, namely, ||v||.

norm(int) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector

Compute the norm of a vector.

NormalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate

The NormalDistribution distribution has its density of a Gaussian function.

NormalDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution

Construct a standard NormalDistribution distribution instance with mean 0 and standard deviation 1.

NormalDistribution(double, double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution

Construct a NormalDistribution distribution instance with mean mu and standard deviation sigma.

normalize() - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial

Normalize this polynomial so that the leading coefficient is 1.

nParams() - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.ConditionalSumOfSquares

Compute the number of parameters for the estimation/fitting.

nPoints4Extrapolation - Variable in class com.numericalmethod.suanshu.analysis.differentiation.Ridders

the number of points for extrapolation

nrows - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination

number of rows

nrows - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt

number of rows

nrows - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection

number of rows

nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix

nrows - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel

number of rows

nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixMathImpl

nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct

nRows() - Method in class com.numericalmethod.suanshu.matrix.doubles.operation.SubMatrixRef

nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix

nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix

nRows() - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix

nRows() - Method in interface com.numericalmethod.suanshu.matrix.MatrixDimension

Get the number of rows.

nRows() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau

Get the number of basic variables.

nrows() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1

nrows() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma2

Deprecated.

nrows() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion

Get the dimension of the process.

nSim - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation

the number of simulations

nSim - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera

nSim - Variable in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBeraDistribution

the number of Monte Carlo simulation paths

nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution

the number of simulations

nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution1

the number of simulations

nSim - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfFiniteSampleDistribution

the number of simulations

nStates() - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel

Get the number of states.

nSymbols() - Method in class com.numericalmethod.suanshu.stats.hmm.HiddenMarkovModel

Get the number of distinct observation symbols per state.

nT - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution

the number of grid points in interval [0, 1] The bigger nT is, the finer the time discretization is, the smaller the discretization error is, and the more accurate the results are.

nT - Variable in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AdfAsymptoticDistribution1

the number of grid point in interval [0, 1] The bigger nT is, the finer the time discretization is, the smaller the discretization error is, the more accurate the results are.

nullDeviance - Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Residuals

the null deviance

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovSmirnov

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.DAgostino

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.JarqueBera

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.Lilliefors

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilk

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.HypothesisTest

Get a description of the null hypothesis.

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.OneWayANOVA

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.mean.T

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.KruskalWallis

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.SiegelTukey

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.VanDerWaerden

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSum

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRank

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.adf.AugmentedDickeyFuller

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Bartlett

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.F

nullHypothesis() - Method in class com.numericalmethod.suanshu.stats.test.variance.Levene

nullity() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel

Get the nullity of A.

nullity(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.operation.Measure

Deprecated. Not supported yet.

NullMonitor - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative

This IterationMonitor does nothing when a new iterate is added via IterationMonitor.addIterate(com.numericalmethod.suanshu.vector.doubles.Vector).

NullMonitor() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.NullMonitor

NumberList - Class in com.numericalmethod.suanshu.datastructure.list

This data structure represents a list of Numbers.

NumberList() - Constructor for class com.numericalmethod.suanshu.datastructure.list.NumberList

Construct an empty list of numbers.

NumberList(Collection<? extends Number>) - Constructor for class com.numericalmethod.suanshu.datastructure.list.NumberList

Copy constructor to copy from another collection of numbers.

NumberList(Number...) - Constructor for class com.numericalmethod.suanshu.datastructure.list.NumberList

Construct a list of numbers from the inputs.

NumberList(String...) - Constructor for class com.numericalmethod.suanshu.datastructure.list.NumberList

Construct a list of numbers from the inputs.

NumberUtils - Class in com.numericalmethod.suanshu.number

This class collects the utility functions to manipulate data of type Number.

NumberUtils.Comparable<T extends java.lang.Number> - Interface in com.numericalmethod.suanshu.number

We need a precision parameter to determine whether two numbers are close enough to be treated as equal.

NY - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils

New York