Uses of Class
org.apache.commons.math4.legacy.exception.NullArgumentException

Packages that use NullArgumentException

Package	Description
org.apache.commons.math4.legacy.analysis.function	The function package contains function objects that wrap the methods contained in Math, as well as common mathematical functions such as the gaussian and sinc functions.
org.apache.commons.math4.legacy.analysis.interpolation	Univariate real functions interpolation algorithms.
org.apache.commons.math4.legacy.analysis.polynomials	Univariate real polynomials implementations, seen as differentiable univariate real functions.
org.apache.commons.math4.legacy.analysis.solvers	Root finding algorithms, for univariate real functions.
org.apache.commons.math4.legacy.core	Core math utilities.
org.apache.commons.math4.legacy.filter	Implementations of common discrete-time linear filters.
org.apache.commons.math4.legacy.genetics	This package provides Genetic Algorithms components and implementations.
org.apache.commons.math4.legacy.linear	Linear algebra support.
org.apache.commons.math4.legacy.stat	Data storage, manipulation and summary routines.
org.apache.commons.math4.legacy.stat.descriptive	Generic univariate summary statistic objects.
org.apache.commons.math4.legacy.stat.descriptive.moment	Summary statistics based on moments.
org.apache.commons.math4.legacy.stat.descriptive.rank	Summary statistics based on ranks.
org.apache.commons.math4.legacy.stat.descriptive.summary	Other summary statistics.
org.apache.commons.math4.legacy.stat.inference	Classes providing hypothesis testing.
org.apache.commons.math4.legacy.util	Convenience routines and common data structures used throughout the commons-math library.

Uses of NullArgumentException in org.apache.commons.math4.legacy.analysis.function

Methods in org.apache.commons.math4.legacy.analysis.function that throw NullArgumentException

Modifier and Type	Method	Description
double[]	Gaussian.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	HarmonicOscillator.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Logistic.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Logit.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Sigmoid.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double	Gaussian.Parametric.value(double x, double... param)	Computes the value of the Gaussian at x.
double	HarmonicOscillator.Parametric.value(double x, double... param)	Computes the value of the harmonic oscillator at x.
double	Logistic.Parametric.value(double x, double... param)	Computes the value of the sigmoid at x.
double	Logit.Parametric.value(double x, double... param)	Computes the value of the logit at x.
double	Sigmoid.Parametric.value(double x, double... param)	Computes the value of the sigmoid at x.

Constructors in org.apache.commons.math4.legacy.analysis.function that throw NullArgumentException

Constructor	Description
StepFunction(double[] x, double[] y)	Builds a step function from a list of arguments and the corresponding values.

Uses of NullArgumentException in org.apache.commons.math4.legacy.analysis.interpolation

Methods in org.apache.commons.math4.legacy.analysis.interpolation that throw NullArgumentException

Modifier and Type	Method	Description
void	FieldHermiteInterpolator.addSamplePoint(T x, T[]... value)	Add a sample point.
T[][]	FieldHermiteInterpolator.derivatives(T x, int order)	Interpolate value and first derivatives at a specified abscissa.
MultivariateFunction	MicrosphereProjectionInterpolator.interpolate(double[][] xval, double[] yval)	Computes an interpolating function for the data set.
MultivariateFunction	MultivariateInterpolator.interpolate(double[][] xval, double[] yval)	Computes an interpolating function for the data set.
PiecewiseBicubicSplineInterpolatingFunction	PiecewiseBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)	Compute an interpolating function for the dataset.
T[]	FieldHermiteInterpolator.value(T x)	Interpolate value at a specified abscissa.

Constructors in org.apache.commons.math4.legacy.analysis.interpolation that throw NullArgumentException

Constructor	Description
PiecewiseBicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f)

Uses of NullArgumentException in org.apache.commons.math4.legacy.analysis.polynomials

Methods in org.apache.commons.math4.legacy.analysis.polynomials that throw NullArgumentException

Modifier and Type	Method	Description
protected static double[]	PolynomialFunction.differentiate(double[] coefficients)	Returns the coefficients of the derivative of the polynomial with the given coefficients.
protected static double	PolynomialFunction.evaluate(double[] coefficients, double argument)	Uses Horner's Method to evaluate the polynomial with the given coefficients at the argument.
static double	PolynomialFunctionNewtonForm.evaluate(double[] a, double[] c, double z)	Evaluate the Newton polynomial using nested multiplication.
DerivativeStructure	PolynomialFunction.value(DerivativeStructure t)	Simple mathematical function.
protected static void	PolynomialFunctionNewtonForm.verifyInputArray(double[] a, double[] c)	Verifies that the input arrays are valid.

Constructors in org.apache.commons.math4.legacy.analysis.polynomials that throw NullArgumentException

Constructor	Description
PolynomialFunction(double[] c)	Construct a polynomial with the given coefficients.
PolynomialFunctionNewtonForm(double[] a, double[] c)	Construct a Newton polynomial with the given a[] and c[].
PolynomialSplineFunction(double[] knots, PolynomialFunction[] polynomials)	Construct a polynomial spline function with the given segment delimiters and interpolating polynomials.

Uses of NullArgumentException in org.apache.commons.math4.legacy.analysis.solvers

Methods in org.apache.commons.math4.legacy.analysis.solvers that throw NullArgumentException

Modifier and Type	Method	Description
static double[]	UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0 and maximumIterations set to Integer.MAX_VALUE.
static double[]	UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0.
static boolean	UnivariateSolverUtils.isBracketing(UnivariateFunction function, double lower, double upper)	Check whether the interval bounds bracket a root.
T	FieldBracketingNthOrderBrentSolver.solve(int maxEval, RealFieldUnivariateFunction<T> f, T min, T max, AllowedSolution allowedSolution)	Solve for a zero in the given interval.
T	FieldBracketingNthOrderBrentSolver.solve(int maxEval, RealFieldUnivariateFunction<T> f, T min, T max, T startValue, AllowedSolution allowedSolution)	Solve for a zero in the given interval, start at startValue.
static double	UnivariateSolverUtils.solve(UnivariateFunction function, double x0, double x1)	Convenience method to find a zero of a univariate real function.
static double	UnivariateSolverUtils.solve(UnivariateFunction function, double x0, double x1, double absoluteAccuracy)	Convenience method to find a zero of a univariate real function.
org.apache.commons.numbers.complex.Complex[]	LaguerreSolver.solveAllComplex(double[] coefficients, double initial)	Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.
org.apache.commons.numbers.complex.Complex	LaguerreSolver.solveComplex(double[] coefficients, double initial)	Find a complex root for the polynomial with the given coefficients, starting from the given initial value.
static void	UnivariateSolverUtils.verifyBracketing(UnivariateFunction function, double lower, double upper)	Check that the endpoints specify an interval and the end points bracket a root.

Uses of NullArgumentException in org.apache.commons.math4.legacy.core

Methods in org.apache.commons.math4.legacy.core that throw NullArgumentException

Modifier and Type	Method	Description
T	FieldElement.add(T a)	Compute this + a.
T	FieldElement.divide(T a)	Compute this ÷ a.
T	FieldElement.multiply(T a)	Compute this × a.
T	FieldElement.subtract(T a)	Compute this - a.

Uses of NullArgumentException in org.apache.commons.math4.legacy.filter

Methods in org.apache.commons.math4.legacy.filter that throw NullArgumentException

Modifier and Type	Method	Description
void	KalmanFilter.correct(double[] z)	Correct the current state estimate with an actual measurement.
void	KalmanFilter.correct(RealVector z)	Correct the current state estimate with an actual measurement.

Constructors in org.apache.commons.math4.legacy.filter that throw NullArgumentException

Constructor	Description
DefaultMeasurementModel(double[][] measMatrix, double[][] measNoise)	Create a new MeasurementModel, taking double arrays as input parameters for the respective measurement matrix and noise.
DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise)	Create a new ProcessModel, taking double arrays as input parameters.
DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance)	Create a new ProcessModel, taking double arrays as input parameters.
KalmanFilter(ProcessModel process, MeasurementModel measurement)	Creates a new Kalman filter with the given process and measurement models.

Uses of NullArgumentException in org.apache.commons.math4.legacy.genetics

Constructors in org.apache.commons.math4.legacy.genetics that throw NullArgumentException

Constructor	Description
ElitisticListPopulation(List<Chromosome> chromosomes, int populationLimit, double elitismRate)	Creates a new ElitisticListPopulation instance.
ListPopulation(List<Chromosome> chromosomes, int populationLimit)	Creates a new ListPopulation instance.

Uses of NullArgumentException in org.apache.commons.math4.legacy.linear

Methods in org.apache.commons.math4.legacy.linear that throw NullArgumentException

Modifier and Type	Method	Description
FieldVector<T>	SparseFieldVector.append(T d)	Construct a vector by appending a T to this vector.
protected static void	IterativeLinearSolver.checkParameters(RealLinearOperator a, RealVector b, RealVector x0)	Performs all dimension checks on the parameters of solve and solveInPlace, and throws an exception if one of the checks fails.
protected static void	PreconditionedIterativeLinearSolver.checkParameters(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Performs all dimension checks on the parameters of solve and solveInPlace, and throws an exception if one of the checks fails.
protected void	AbstractFieldMatrix.checkSubMatrixIndex(int[] selectedRows, int[] selectedColumns)	Check if submatrix ranges indices are valid.
static void	MatrixUtils.checkSubMatrixIndex(AnyMatrix m, int[] selectedRows, int[] selectedColumns)	Check if submatrix ranges indices are valid.
void	AbstractFieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)	Copy a submatrix.
void	AbstractRealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)	Copy a submatrix.
void	FieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)	Copy a submatrix.
void	RealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)	Copy a submatrix.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createColumnFieldMatrix(T[] columnData)	Creates a column FieldMatrix using the data from the input array.
static RealMatrix	MatrixUtils.createColumnRealMatrix(double[] columnData)	Creates a column RealMatrix using the data from the input array.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createFieldMatrix(T[][] data)	Returns a FieldMatrix whose entries are the values in the the input array.
static <T extends FieldElement<T>> FieldVector<T>	MatrixUtils.createFieldVector(T[] data)	Creates a FieldVector using the data from the input array.
static RealMatrix	MatrixUtils.createRealMatrix(double[][] data)	Returns a RealMatrix whose entries are the values in the the input array.
static RealVector	MatrixUtils.createRealVector(double[] data)	Creates a RealVector using the data from the input array.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createRowFieldMatrix(T[] rowData)	Create a row FieldMatrix using the data from the input array.
static RealMatrix	MatrixUtils.createRowRealMatrix(double[] rowData)	Create a row RealMatrix using the data from the input array.
protected static <T extends FieldElement<T>> Field<T>	AbstractFieldMatrix.extractField(T[][] d)	Get the elements type from an array.
FieldMatrix<T>	AbstractFieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Get a submatrix.
RealMatrix	AbstractRealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Gets a submatrix.
FieldMatrix<T>	FieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Get a submatrix.
RealMatrix	RealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Gets a submatrix.
static RealMatrix	MatrixUtils.inverse(RealMatrix matrix)	Computes the inverse of the given matrix.
static RealMatrix	MatrixUtils.inverse(RealMatrix matrix, double threshold)	Computes the inverse of the given matrix.
FieldVector<T>	ArrayFieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	FieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	SparseFieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	ArrayFieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	FieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	SparseFieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	ArrayFieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	FieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	SparseFieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	ArrayFieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	FieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	SparseFieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	ArrayFieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	FieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	SparseFieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	ArrayFieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	FieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	SparseFieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	ArrayFieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	FieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	SparseFieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	ArrayFieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
FieldVector<T>	FieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
FieldVector<T>	SparseFieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
void	SparseFieldVector.setEntry(int index, T value)	Set a single element.
void	AbstractFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	AbstractRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
void	Array2DRowFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	BlockFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	BlockRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
void	FieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	RealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, boolean goodb, double shift)	Returns the solution to the system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	ConjugateGradient.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	IterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.

Constructors in org.apache.commons.math4.legacy.linear that throw NullArgumentException

Constructor	Description
Array2DRowFieldMatrix(Field<T> field, T[][] d)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(Field<T> field, T[][] d, boolean copyArray)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(T[][] d)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(T[][] d, boolean copyArray)	Create a new FieldMatrix<T> using the input array as the underlying data array.
ArrayFieldVector(Field<T> field, T[] d)	Construct a vector from an array, copying the input array.
ArrayFieldVector(Field<T> field, T[] d, boolean copyArray)	Create a new ArrayFieldVector using the input array as the underlying data array.
ArrayFieldVector(Field<T> field, T[] d, int pos, int size)	Construct a vector from part of a array.
ArrayFieldVector(Field<T> field, T[] v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(ArrayFieldVector<T> v)	Construct a vector from another vector, using a deep copy.
ArrayFieldVector(ArrayFieldVector<T> v, boolean deep)	Construct a vector from another vector.
ArrayFieldVector(FieldVector<T> v)	Construct a vector from another vector, using a deep copy.
ArrayFieldVector(FieldVector<T> v1, FieldVector<T> v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(FieldVector<T> v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(T[] d)	Construct a vector from an array, copying the input array.
ArrayFieldVector(T[] d, boolean copyArray)	Create a new ArrayFieldVector using the input array as the underlying data array.
ArrayFieldVector(T[] d, int pos, int size)	Construct a vector from part of a array.
ArrayFieldVector(T[] v1, FieldVector<T> v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(T[] v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayRealVector(double[] d, boolean copyArray)	Create a new ArrayRealVector using the input array as the underlying data array.
ArrayRealVector(double[] d, int pos, int size)	Construct a vector from part of a array.
ArrayRealVector(Double[] d, int pos, int size)	Construct a vector from part of an array.
ArrayRealVector(ArrayRealVector v)	Construct a vector from another vector, using a deep copy.
ArrayRealVector(RealVector v)	Construct a vector from another vector, using a deep copy.
ConjugateGradient(IterationManager manager, double delta, boolean check)	Creates a new instance of this class, with default stopping criterion and custom iteration manager.
DiagonalMatrix(double[] d, boolean copyArray)	Creates a matrix using the input array as the underlying data.
IterativeLinearSolver(IterationManager manager)	Creates a new instance of this class, with custom iteration manager.
PreconditionedIterativeLinearSolver(IterationManager manager)	Creates a new instance of this class, with custom iteration manager.
SparseFieldVector(Field<T> field, T[] values)	Create from a Field array.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat

Methods in org.apache.commons.math4.legacy.stat that throw NullArgumentException

Modifier and Type	Method	Description
void	Frequency.merge(Collection<Frequency<T>> others)	Merge a Collection of Frequency objects into this instance.
void	Frequency.merge(Frequency<T> other)	Merge another Frequency object's counts into this instance.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat.descriptive

Methods in org.apache.commons.math4.legacy.stat.descriptive that throw NullArgumentException

Modifier and Type	Method	Description
static void	DescriptiveStatistics.copy(DescriptiveStatistics source, DescriptiveStatistics dest)	Copies source to dest.
static void	SummaryStatistics.copy(SummaryStatistics source, SummaryStatistics dest)	Copies source to dest.
static void	SynchronizedDescriptiveStatistics.copy(SynchronizedDescriptiveStatistics source, SynchronizedDescriptiveStatistics dest)	Copies source to dest.
static void	SynchronizedSummaryStatistics.copy(SynchronizedSummaryStatistics source, SynchronizedSummaryStatistics dest)	Copies source to dest.

Constructors in org.apache.commons.math4.legacy.stat.descriptive that throw NullArgumentException

Constructor	Description
AggregateSummaryStatistics(SummaryStatistics prototypeStatistics)	Initializes a new AggregateSummaryStatistics with the specified statistics object as a prototype for contributing statistics and for the internal aggregate statistics.
DescriptiveStatistics(DescriptiveStatistics original)	Copy constructor.
SummaryStatistics(SummaryStatistics original)	A copy constructor.
SynchronizedDescriptiveStatistics(SynchronizedDescriptiveStatistics original)	A copy constructor.
SynchronizedSummaryStatistics(SynchronizedSummaryStatistics original)	A copy constructor.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat.descriptive.moment

Methods in org.apache.commons.math4.legacy.stat.descriptive.moment that throw NullArgumentException

Modifier and Type	Method	Description
static void	GeometricMean.copy(GeometricMean source, GeometricMean dest)	Copies source to dest.
static void	Kurtosis.copy(Kurtosis source, Kurtosis dest)	Copies source to dest.
static void	Mean.copy(Mean source, Mean dest)	Copies source to dest.
static void	SecondMoment.copy(SecondMoment source, SecondMoment dest)	Copies source to dest.
static void	SemiVariance.copy(SemiVariance source, SemiVariance dest)	Copies source to dest.
static void	Skewness.copy(Skewness source, Skewness dest)	Copies source to dest.
static void	StandardDeviation.copy(StandardDeviation source, StandardDeviation dest)	Copies source to dest.
static void	Variance.copy(Variance source, Variance dest)	Copies source to dest.

Constructors in org.apache.commons.math4.legacy.stat.descriptive.moment that throw NullArgumentException

Constructor	Description
GeometricMean(GeometricMean original)	Copy constructor, creates a new GeometricMean identical to the original.
Kurtosis(Kurtosis original)	Copy constructor, creates a new Kurtosis identical to the original.
Mean(Mean original)	Copy constructor, creates a new Mean identical to the original.
SecondMoment(SecondMoment original)	Copy constructor, creates a new SecondMoment identical to the original.
SemiVariance(SemiVariance original)	Copy constructor, creates a new SemiVariance identical to the original.
Skewness(Skewness original)	Copy constructor, creates a new Skewness identical to the original.
StandardDeviation(StandardDeviation original)	Copy constructor, creates a new StandardDeviation identical to the original.
Variance(Variance original)	Copy constructor, creates a new Variance identical to the original.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat.descriptive.rank

Methods in org.apache.commons.math4.legacy.stat.descriptive.rank that throw NullArgumentException

Modifier and Type	Method	Description
static void	Max.copy(Max source, Max dest)	Copies source to dest.
static void	Min.copy(Min source, Min dest)	Copies source to dest.

Constructors in org.apache.commons.math4.legacy.stat.descriptive.rank that throw NullArgumentException

Constructor	Description
Max(Max original)	Copy constructor, creates a new Max identical to the original.
Median(Median original)	Copy constructor, creates a new Median identical.
Min(Min original)	Copy constructor, creates a new Min identical to the original.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat.descriptive.summary

Methods in org.apache.commons.math4.legacy.stat.descriptive.summary that throw NullArgumentException

Modifier and Type	Method	Description
static void	Product.copy(Product source, Product dest)	Copies source to dest.
static void	Sum.copy(Sum source, Sum dest)	Copies source to dest.
static void	SumOfLogs.copy(SumOfLogs source, SumOfLogs dest)	Copies source to dest.
static void	SumOfSquares.copy(SumOfSquares source, SumOfSquares dest)	Copies source to dest.

Constructors in org.apache.commons.math4.legacy.stat.descriptive.summary that throw NullArgumentException

Constructor	Description
Product(Product original)	Copy constructor, creates a new Product identical to the original.
Sum(Sum original)	Copy constructor, creates a new Sum identical to the original.
SumOfLogs(SumOfLogs original)	Copy constructor, creates a new SumOfLogs identical to the original.
SumOfSquares(SumOfSquares original)	Copy constructor, creates a new SumOfSquares identical to the original.

Uses of NullArgumentException in org.apache.commons.math4.legacy.stat.inference

Methods in org.apache.commons.math4.legacy.stat.inference that throw NullArgumentException

Modifier and Type	Method	Description
double	OneWayAnova.anovaFValue(Collection<double[]> categoryData)	Computes the ANOVA F-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(Collection<double[]> categoryData)	Computes the ANOVA P-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(Collection<SummaryStatistics> categoryData, boolean allowOneElementData)	Computes the ANOVA P-value for a collection of SummaryStatistics.
boolean	OneWayAnova.anovaTest(Collection<double[]> categoryData, double alpha)	Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
double	ChiSquareTest.chiSquare(long[][] counts)	Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
static double	InferenceTestUtils.chiSquare(long[][] counts)
double	ChiSquareTest.chiSquareTest(long[][] counts)	Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
boolean	ChiSquareTest.chiSquareTest(long[][] counts, double alpha)	Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
static double	InferenceTestUtils.chiSquareTest(long[][] counts)
static boolean	InferenceTestUtils.chiSquareTest(long[][] counts, double alpha)
static double	InferenceTestUtils.homoscedasticT(double[] sample1, double[] sample2)
static double	InferenceTestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.homoscedasticT(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
double	TTest.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
static double	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
static double	InferenceTestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.homoscedasticTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
boolean	TTest.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
double	TTest.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
static double	InferenceTestUtils.kolmogorovSmirnovStatistic(double[] x, double[] y)
static double	InferenceTestUtils.kolmogorovSmirnovStatistic(org.apache.commons.statistics.distribution.ContinuousDistribution dist, double[] data)
static double	InferenceTestUtils.kolmogorovSmirnovTest(double[] x, double[] y)
static double	InferenceTestUtils.kolmogorovSmirnovTest(double[] x, double[] y, boolean strict)
static double	InferenceTestUtils.kolmogorovSmirnovTest(org.apache.commons.statistics.distribution.ContinuousDistribution dist, double[] data)
static double	InferenceTestUtils.kolmogorovSmirnovTest(org.apache.commons.statistics.distribution.ContinuousDistribution dist, double[] data, boolean strict)
static boolean	InferenceTestUtils.kolmogorovSmirnovTest(org.apache.commons.statistics.distribution.ContinuousDistribution dist, double[] data, double alpha)
double	MannWhitneyUTest.mannWhitneyU(double[] x, double[] y)	Computes the Mann-Whitney U statistic comparing mean for two independent samples possibly of different length.
double	MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y)	Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples.
static double	InferenceTestUtils.oneWayAnovaFValue(Collection<double[]> categoryData)
static double	InferenceTestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)
static boolean	InferenceTestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)
static double	InferenceTestUtils.pairedT(double[] sample1, double[] sample2)
double	TTest.pairedT(double[] sample1, double[] sample2)	Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
double	TTest.pairedTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
boolean	TTest.pairedTTest(double[] sample1, double[] sample2, double alpha)	Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
static double	InferenceTestUtils.t(double[] sample1, double[] sample2)
static double	InferenceTestUtils.t(double mu, double[] observed)
static double	InferenceTestUtils.t(double mu, StatisticalSummary sampleStats)
static double	InferenceTestUtils.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	TTest.t(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
double	TTest.t(double mu, double[] observed)	Computes a t statistic given observed values and a comparison constant.
double	TTest.t(double mu, StatisticalSummary sampleStats)	Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
double	TTest.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
static double	InferenceTestUtils.tTest(double[] sample1, double[] sample2)
static boolean	InferenceTestUtils.tTest(double[] sample1, double[] sample2, double alpha)
static double	InferenceTestUtils.tTest(double mu, double[] sample)
static boolean	InferenceTestUtils.tTest(double mu, double[] sample, double alpha)
static double	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats)
static boolean	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
static double	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
static boolean	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
double	TTest.tTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
boolean	TTest.tTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
double	TTest.tTest(double mu, double[] sample)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
boolean	TTest.tTest(double mu, double[] sample, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
double	TTest.tTest(double mu, StatisticalSummary sampleStats)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
boolean	TTest.tTest(double mu, StatisticalSummary sampleStats, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
double	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
boolean	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
double	WilcoxonSignedRankTest.wilcoxonSignedRank(double[] x, double[] y)	Computes the Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.
double	WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue)	Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.

Uses of NullArgumentException in org.apache.commons.math4.legacy.util

Methods in org.apache.commons.math4.legacy.util that throw NullArgumentException

Modifier and Type	Method	Description
static ComplexFormat	ComplexFormat.getInstance(String imaginaryCharacter, Locale locale)	Returns the default complex format for the given locale.

Constructors in org.apache.commons.math4.legacy.util that throw NullArgumentException

Constructor	Description
ComplexFormat(String imaginaryCharacter)	Create an instance with a custom imaginary character, and the default number format for both real and imaginary parts.
ComplexFormat(String imaginaryCharacter, NumberFormat format)	Create an instance with a custom imaginary character, and a custom number format for both real and imaginary parts.
ComplexFormat(String imaginaryCharacter, NumberFormat realFormat, NumberFormat imaginaryFormat)	Create an instance with a custom imaginary character, a custom number format for the real part, and a custom number format for the imaginary part.
ComplexFormat(NumberFormat format)	Create an instance with a custom number format for both real and imaginary parts.
ComplexFormat(NumberFormat realFormat, NumberFormat imaginaryFormat)	Create an instance with a custom number format for the real part and a custom number format for the imaginary part.