# Uses of Class
org.apache.commons.math3.exception.NullArgumentException

Packages that use NullArgumentException
Package
Description
org.apache.commons.math3
Common classes used throughout the commons-math library.
org.apache.commons.math3.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.math3.analysis.integration
Numerical integration (quadrature) algorithms for univariate real functions.
org.apache.commons.math3.analysis.interpolation
Univariate real functions interpolation algorithms.
org.apache.commons.math3.analysis.polynomials
Univariate real polynomials implementations, seen as differentiable univariate real functions.
org.apache.commons.math3.analysis.solvers
Root finding algorithms, for univariate real functions.
org.apache.commons.math3.complex
Complex number type and implementations of complex transcendental functions.
org.apache.commons.math3.dfp
Decimal floating point library for Java
org.apache.commons.math3.filter
Implementations of common discrete-time linear filters.
org.apache.commons.math3.fraction
Fraction number type and fraction number formatting.
org.apache.commons.math3.genetics
This package provides Genetic Algorithms components and implementations.
org.apache.commons.math3.geometry.euclidean.twod.hull
This package provides algorithms to generate the convex hull for a set of points in an two-dimensional euclidean space.
org.apache.commons.math3.geometry.hull
This package provides interfaces and classes related to the convex hull problem.
org.apache.commons.math3.linear
Linear algebra support.
org.apache.commons.math3.ml.clustering
Clustering algorithms.
org.apache.commons.math3.optim.nonlinear.scalar
Algorithms for optimizing a scalar function.
org.apache.commons.math3.optim.nonlinear.vector
Algorithms for optimizing a vector function.
org.apache.commons.math3.random
Random number and random data generators.
org.apache.commons.math3.stat
Data storage, manipulation and summary routines.
org.apache.commons.math3.stat.clustering
All classes and sub-packages of this package are deprecated.
org.apache.commons.math3.stat.descriptive
Generic univariate summary statistic objects.
org.apache.commons.math3.stat.descriptive.moment
Summary statistics based on moments.
org.apache.commons.math3.stat.descriptive.rank
Summary statistics based on ranks.
org.apache.commons.math3.stat.descriptive.summary
Other summary statistics.
org.apache.commons.math3.stat.inference
Classes providing hypothesis testing.
org.apache.commons.math3.util
Convenience routines and common data structures used throughout the commons-math library.

## Uses of NullArgumentException in org.apache.commons.math3

Methods in org.apache.commons.math3 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.math3.analysis.function

Methods in org.apache.commons.math3.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.math3.analysis.function that throw NullArgumentException
Modifier
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.math3.analysis.integration

Methods in org.apache.commons.math3.analysis.integration that throw NullArgumentException
Modifier and Type
Method
Description
`double`
BaseAbstractUnivariateIntegrator.`integrate(int maxEval, UnivariateFunction f, double lower, double upper)`
Integrate the function in the given interval.
`double`
UnivariateIntegrator.`integrate(int maxEval, UnivariateFunction f, double min, double max)`
Integrate the function in the given interval.
`protected void`
BaseAbstractUnivariateIntegrator.`setup(int maxEval, UnivariateFunction f, double lower, double upper)`
Prepare for computation.

## Uses of NullArgumentException in org.apache.commons.math3.analysis.interpolation

Methods in org.apache.commons.math3.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`
MicrosphereInterpolator.`interpolate(double[][] xval, double[] yval)`
Deprecated.
Computes an interpolating function for the data set.
`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.
`BicubicSplineInterpolatingFunction`
SmoothingPolynomialBicubicSplineInterpolator.`interpolate(double[] xval, double[] yval, double[][] fval)`
Deprecated.
Compute an interpolating function for the dataset.
`T[]`
FieldHermiteInterpolator.`value(T x)`
Interpolate value at a specified abscissa.
Constructors in org.apache.commons.math3.analysis.interpolation that throw NullArgumentException
Modifier
Constructor
Description
``
`MicrosphereInterpolatingFunction(double[][] xval, double[] yval, int brightnessExponent, int microsphereElements, UnitSphereRandomVectorGenerator rand)`
Deprecated.
``
`PiecewiseBicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f)`

## Uses of NullArgumentException in org.apache.commons.math3.analysis.polynomials

Methods in org.apache.commons.math3.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.math3.analysis.polynomials that throw NullArgumentException
Modifier
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.math3.analysis.solvers

Methods in org.apache.commons.math3.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.
`protected void`
BaseAbstractUnivariateSolver.`setup(int maxEval, FUNC f, double min, double max, double startValue)`
Prepare for computation.
`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.
`Complex[]`
LaguerreSolver.`solveAllComplex(double[] coefficients, double initial)`
Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.
`Complex[]`
LaguerreSolver.`solveAllComplex(double[] coefficients, double initial, int maxEval)`
Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.
`Complex`
LaguerreSolver.`solveComplex(double[] coefficients, double initial)`
Find a complex root for the polynomial with the given coefficients, starting from the given initial value.
`Complex`
LaguerreSolver.`solveComplex(double[] coefficients, double initial, int maxEval)`
Find a complex root for the polynomial with the given coefficients, starting from the given initial value.
`protected void`
BaseAbstractUnivariateSolver.`verifyBracketing(double lower, double upper)`
Check that the endpoints specify an interval and the function takes opposite signs at the endpoints.
`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.math3.complex

Methods in org.apache.commons.math3.complex that throw NullArgumentException
Modifier and Type
Method
Description
`Complex`
Complex.`add(Complex addend)`
Returns a `Complex` whose value is `(this + addend)`.
`Complex`
Complex.`divide(Complex divisor)`
Returns a `Complex` whose value is `(this / divisor)`.
`static ComplexFormat`
ComplexFormat.`getInstance(String imaginaryCharacter, Locale locale)`
Returns the default complex format for the given locale.
`Complex`
Complex.`multiply(Complex factor)`
Returns a `Complex` whose value is `this * factor`.
`Complex`
Complex.`pow(Complex x)`
Returns of value of this complex number raised to the power of `x`.
`Complex`
Complex.`subtract(Complex subtrahend)`
Returns a `Complex` whose value is `(this - subtrahend)`.
Constructors in org.apache.commons.math3.complex that throw NullArgumentException
Modifier
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.

## Uses of NullArgumentException in org.apache.commons.math3.dfp

Methods in org.apache.commons.math3.dfp that throw NullArgumentException
Modifier and Type
Method
Description
`Dfp`
BracketingNthOrderBrentSolverDFP.`solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, AllowedSolution allowedSolution)`
Deprecated.
Solve for a zero in the given interval.
`Dfp`
BracketingNthOrderBrentSolverDFP.`solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, Dfp startValue, AllowedSolution allowedSolution)`
Deprecated.
Solve for a zero in the given interval, start at `startValue`.

## Uses of NullArgumentException in org.apache.commons.math3.filter

Methods in org.apache.commons.math3.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.math3.filter that throw NullArgumentException
Modifier
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.math3.fraction

Methods in org.apache.commons.math3.fraction that throw NullArgumentException
Modifier and Type
Method
Description
`BigFraction`
BigFraction.`add(BigInteger bg)`
Adds the value of this fraction to the passed `BigInteger`, returning the result in reduced form.

## Uses of NullArgumentException in org.apache.commons.math3.genetics

Methods in org.apache.commons.math3.genetics that throw NullArgumentException
Modifier and Type
Method
Description
`void`
ListPopulation.`setChromosomes(List<Chromosome> chromosomes)`
Deprecated.
use `ListPopulation.addChromosomes(Collection)` instead
Constructors in org.apache.commons.math3.genetics that throw NullArgumentException
Modifier
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.math3.geometry.euclidean.twod.hull

Methods in org.apache.commons.math3.geometry.euclidean.twod.hull that throw NullArgumentException
Modifier and Type
Method
Description
`ConvexHull2D`
ConvexHullGenerator2D.`generate(Collection<Vector2D> points)`
Builds the convex hull from the set of input points.

## Uses of NullArgumentException in org.apache.commons.math3.geometry.hull

Methods in org.apache.commons.math3.geometry.hull that throw NullArgumentException
Modifier and Type
Method
Description
`ConvexHull<S,P>`
ConvexHullGenerator.`generate(Collection<P> points)`
Builds the convex hull from the set of input points.

## Uses of NullArgumentException in org.apache.commons.math3.linear

Methods in org.apache.commons.math3.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 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 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`
Array2DRowRealMatrix.`setSubMatrix(double[][] 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.math3.linear that throw NullArgumentException
Modifier
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.
``
`Array2DRowRealMatrix(double[][] d)`
Create a new `RealMatrix` using the input array as the underlying data array.
``
`Array2DRowRealMatrix(double[][] d, boolean copyArray)`
Create a new RealMatrix 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(ArrayFieldVector<T> v1, ArrayFieldVector<T> v2)`
Deprecated.
as of 3.2, replaced by `ArrayFieldVector(FieldVector, FieldVector)`
``
`ArrayFieldVector(ArrayFieldVector<T> v1, T[] v2)`
Deprecated.
as of 3.2, replaced by `ArrayFieldVector(FieldVector, FieldElement[])`
``
`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, ArrayFieldVector<T> v2)`
Deprecated.
as of 3.2, replaced by `ArrayFieldVector(FieldElement[], FieldVector)`
``
`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.math3.ml.clustering

Methods in org.apache.commons.math3.ml.clustering that throw NullArgumentException
Modifier and Type
Method
Description
`List<Cluster<T>>`
DBSCANClusterer.`cluster(Collection<T> points)`
Performs DBSCAN cluster analysis.

## Uses of NullArgumentException in org.apache.commons.math3.optim.nonlinear.scalar

Constructors in org.apache.commons.math3.optim.nonlinear.scalar that throw NullArgumentException
Modifier
Constructor
Description
``
`MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer, int starts, RandomVectorGenerator generator)`
Create a multi-start optimizer from a single-start optimizer.

## Uses of NullArgumentException in org.apache.commons.math3.optim.nonlinear.vector

Constructors in org.apache.commons.math3.optim.nonlinear.vector that throw NullArgumentException
Modifier
Constructor
Description
``
`MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer, int starts, RandomVectorGenerator generator)`
Deprecated.
Create a multi-start optimizer from a single-start optimizer.

## Uses of NullArgumentException in org.apache.commons.math3.random

Methods in org.apache.commons.math3.random that throw NullArgumentException
Modifier and Type
Method
Description
`void`
ValueServer.`computeDistribution()`
Computes the empirical distribution using values from the file in `valuesFileURL`, using the default number of bins.
`void`
ValueServer.`computeDistribution(int binCount)`
Computes the empirical distribution using values from the file in `valuesFileURL` and `binCount` bins.
`void`
EmpiricalDistribution.`load(double[] in)`
Computes the empirical distribution from the provided array of numbers.
`void`
EmpiricalDistribution.`load(File file)`
Computes the empirical distribution from the input file.
`void`
EmpiricalDistribution.`load(URL url)`
Computes the empirical distribution using data read from a URL.
Constructors in org.apache.commons.math3.random that throw NullArgumentException
Modifier
Constructor
Description
``
`HaltonSequenceGenerator(int dimension, int[] bases, int[] weights)`
Construct a new Halton sequence generator with the given base numbers and weights for each dimension.
``
`StableRandomGenerator(RandomGenerator generator, double alpha, double beta)`
Create a new generator.

## Uses of NullArgumentException in org.apache.commons.math3.stat

Methods in org.apache.commons.math3.stat that throw NullArgumentException
Modifier and Type
Method
Description
`void`
Frequency.`merge(Collection<Frequency> others)`
Merge a `Collection` of `Frequency` objects into this instance.
`void`
Frequency.`merge(Frequency other)`
Merge another Frequency object's counts into this instance.

## Uses of NullArgumentException in org.apache.commons.math3.stat.clustering

Methods in org.apache.commons.math3.stat.clustering that throw NullArgumentException
Modifier and Type
Method
Description
`List<Cluster<T>>`
DBSCANClusterer.`cluster(Collection<T> points)`
Deprecated.
Performs DBSCAN cluster analysis.

## Uses of NullArgumentException in org.apache.commons.math3.stat.descriptive

Methods in org.apache.commons.math3.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.math3.stat.descriptive that throw NullArgumentException
Modifier
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.math3.stat.descriptive.moment

Methods in org.apache.commons.math3.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.math3.stat.descriptive.moment that throw NullArgumentException
Modifier
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.math3.stat.descriptive.rank

Methods in org.apache.commons.math3.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.math3.stat.descriptive.rank that throw NullArgumentException
Modifier
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 to the `original`
``
`Min(Min original)`
Copy constructor, creates a new `Min` identical to the `original`
``
`Percentile(Percentile original)`
Copy constructor, creates a new `Percentile` identical to the `original`

## Uses of NullArgumentException in org.apache.commons.math3.stat.descriptive.summary

Methods in org.apache.commons.math3.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.math3.stat.descriptive.summary that throw NullArgumentException
Modifier
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.math3.stat.inference

Methods in org.apache.commons.math3.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`
TestUtils.`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`
TestUtils.`chiSquareTest(long[][] counts)`
`static boolean`
TestUtils.`chiSquareTest(long[][] counts, double alpha)`
`static double`
TestUtils.`homoscedasticT(double[] sample1, double[] sample2)`
`static double`
TestUtils.`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`
TestUtils.`homoscedasticTTest(double[] sample1, double[] sample2)`
`static boolean`
TestUtils.`homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
`static double`
TestUtils.`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`
TestUtils.`kolmogorovSmirnovStatistic(double[] x, double[] y)`
`static double`
TestUtils.`kolmogorovSmirnovStatistic(RealDistribution dist, double[] data)`
`static double`
TestUtils.`kolmogorovSmirnovTest(double[] x, double[] y)`
`static double`
TestUtils.`kolmogorovSmirnovTest(double[] x, double[] y, boolean strict)`
`static double`
TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data)`
`static double`
TestUtils.`kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)`
`static boolean`
TestUtils.`kolmogorovSmirnovTest(RealDistribution 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`
TestUtils.`oneWayAnovaFValue(Collection<double[]> categoryData)`
`static double`
TestUtils.`oneWayAnovaPValue(Collection<double[]> categoryData)`
`static boolean`
TestUtils.`oneWayAnovaTest(Collection<double[]> categoryData, double alpha)`
`static double`
TestUtils.`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`
TestUtils.`pairedTTest(double[] sample1, double[] sample2)`
`static boolean`
TestUtils.`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`
TestUtils.`t(double[] sample1, double[] sample2)`
`static double`
TestUtils.`t(double mu, double[] observed)`
`static double`
TestUtils.`t(double mu, StatisticalSummary sampleStats)`
`static double`
TestUtils.`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`
TestUtils.`tTest(double[] sample1, double[] sample2)`
`static boolean`
TestUtils.`tTest(double[] sample1, double[] sample2, double alpha)`
`static double`
TestUtils.`tTest(double mu, double[] sample)`
`static boolean`
TestUtils.`tTest(double mu, double[] sample, double alpha)`
`static double`
TestUtils.`tTest(double mu, StatisticalSummary sampleStats)`
`static boolean`
TestUtils.`tTest(double mu, StatisticalSummary sampleStats, double alpha)`
`static double`
TestUtils.`tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`static boolean`
TestUtils.`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.math3.util

Methods in org.apache.commons.math3.util that throw NullArgumentException
Modifier and Type
Method
Description
`static void`
MathUtils.`checkNotNull(Object o)`
Checks that an object is not null.
`static void`
MathUtils.`checkNotNull(Object o, Localizable pattern, Object... args)`
Checks that an object is not null.
`static void`
MathArrays.`checkRectangular(long[][] in)`
Throws DimensionMismatchException if the input array is not rectangular.
`static double[]`
MathArrays.`convolve(double[] x, double[] h)`
Calculates the convolution between two sequences.
`static void`
ResizableDoubleArray.`copy(ResizableDoubleArray source, ResizableDoubleArray dest)`
Copies source to dest, copying the underlying data, so dest is a new, independent copy of source.
`static void`
MathArrays.`sortInPlace(double[] x, double[]... yList)`
Sort an array in ascending order in place and perform the same reordering of entries on other arrays.
`static void`
MathArrays.`sortInPlace(double[] x, MathArrays.OrderDirection dir, double[]... yList)`
Sort an array in place and perform the same reordering of entries on other arrays.
`double`
DefaultTransformer.`transform(Object o)`
Constructors in org.apache.commons.math3.util that throw NullArgumentException
Modifier
Constructor
Description
``
`Incrementor(int max, Incrementor.MaxCountExceededCallback cb)`
Deprecated.
Defines a maximal count and a callback method to be triggered at counter exhaustion.
``
`KthSelector(PivotingStrategyInterface pivotingStrategy)`
Constructor with specified pivoting strategy
``
`ResizableDoubleArray(ResizableDoubleArray original)`
Copy constructor.