Uses of Class org.apache.commons.math3.exception.NullArgumentException (Apache Commons Math 3.3 API)

## 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 and 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 and Description
    --- | ---
    `double[]` | Sigmoid.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[]` | 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` | Sigmoid.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` | 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`.

    Constructors in org.apache.commons.math3.analysis.function that throw NullArgumentException
    Constructor and 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 and Description
    --- | ---
    `double` | UnivariateIntegrator.`integrate(int maxEval, UnivariateFunction f, double min, double max)` Integrate the function in the given interval.
    `double` | BaseAbstractUnivariateIntegrator.`integrate(int maxEval, UnivariateFunction f, double lower, double upper)` 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 and 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)` Computes an interpolating function for the data set.
    `MultivariateFunction` | MultivariateInterpolator.`interpolate(double[][] xval, double[] yval)` Computes an interpolating function for the data set.
    `BicubicSplineInterpolatingFunction` | SmoothingPolynomialBicubicSplineInterpolator.`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.math3.analysis.interpolation that throw NullArgumentException
    Constructor and Description
    `MicrosphereInterpolatingFunction(double[][] xval, double[] yval, int brightnessExponent, int microsphereElements, UnitSphereRandomVectorGenerator rand)`

  - ### 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 and 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
    Constructor and 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 and 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.
    `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.`solveComplex(double[] coefficients, double initial)` 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 and 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
    Constructor and Description
    `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.
    `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.

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

    Methods in org.apache.commons.math3.dfp that throw NullArgumentException
    Modifier and Type | Method and Description
    --- | ---
    `Dfp` | BracketingNthOrderBrentSolverDFP.`solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, AllowedSolution allowedSolution)` Solve for a zero in the given interval.
    `Dfp` | BracketingNthOrderBrentSolverDFP.`solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, Dfp startValue, AllowedSolution allowedSolution)` 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 and 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
    Constructor and 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 and 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 and Description
    --- | ---
    `void` | ListPopulation.`setChromosomes(List<Chromosome> chromosomes)` Deprecated. use `ListPopulation.addChromosomes(Collection)` instead

    Constructors in org.apache.commons.math3.genetics that throw NullArgumentException
    Constructor and 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 and 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 and 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 and Description
    --- | ---
    `FieldVector<T>` | SparseFieldVector.`append(T d)` Construct a vector by appending a T to this vector.
    `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 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.
    `static void` | MatrixUtils.`checkSubMatrixIndex(AnyMatrix m, int[] selectedRows, int[] selectedColumns)` Check if submatrix ranges indices are valid.
    `protected void` | AbstractFieldMatrix.`checkSubMatrixIndex(int[] selectedRows, int[] selectedColumns)` Check if submatrix ranges indices are valid.
    `void` | AbstractRealMatrix.`copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)` Copy a submatrix.
    `void` | RealMatrix.`copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)` Copy a submatrix.
    `void` | FieldMatrix.`copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)` Copy a submatrix.
    `void` | AbstractFieldMatrix.`copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] 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>` | FieldMatrix.`getSubMatrix(int[] selectedRows, int[] selectedColumns)` Get a submatrix.
    `FieldMatrix<T>` | AbstractFieldMatrix.`getSubMatrix(int[] selectedRows, int[] selectedColumns)` Get a submatrix.
    `RealMatrix` | AbstractRealMatrix.`getSubMatrix(int[] selectedRows, int[] selectedColumns)` Gets 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>` | SparseFieldVector.`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>` | ArrayFieldVector.`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>` | FieldVector.`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>` | SparseFieldVector.`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>` | ArrayFieldVector.`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>` | FieldVector.`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>` | SparseFieldVector.`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>` | ArrayFieldVector.`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>` | FieldVector.`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>` | SparseFieldVector.`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>` | ArrayFieldVector.`mapSubtractToSelf(T d)` Map a subtraction operation to each entry.
    `FieldVector<T>` | SparseFieldVector.`mapSubtractToSelf(T d)` Map a subtraction operation to each entry.
    `FieldVector<T>` | FieldVector.`mapSubtractToSelf(T d)` Map a subtraction operation to each entry.
    `void` | SparseFieldVector.`setEntry(int index, T value)` Set a single element.
    `void` | Array2DRowRealMatrix.`setSubMatrix(double[][] 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` | AbstractRealMatrix.`setSubMatrix(double[][] 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.
    `void` | BlockFieldMatrix.`setSubMatrix(T[][] 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` | AbstractFieldMatrix.`setSubMatrix(T[][] 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.
    `RealVector` | SymmLQ.`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)` 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` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)` 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` | IterativeLinearSolver.`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)` 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` | 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, 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` | 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` | PreconditionedIterativeLinearSolver.`solveInPlace(RealLinearOperator a, RealLinearOperator m, 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, 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.
    `abstract RealVector` | IterativeLinearSolver.`solveInPlace(RealLinearOperator a, 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.

    Constructors in org.apache.commons.math3.linear that throw NullArgumentException
    Constructor and 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(ArrayFieldVector<T> v)` Construct a vector from another vector, using a deep copy.
    `ArrayFieldVector(ArrayFieldVector<T> v1, ArrayFieldVector<T> v2)` Deprecated. as of 3.2, replaced by `ArrayFieldVector.ArrayFieldVector(FieldVector, FieldVector)`
    `ArrayFieldVector(ArrayFieldVector<T> v, boolean deep)` Construct a vector from another vector.
    `ArrayFieldVector(ArrayFieldVector<T> v1, T[] v2)` Deprecated. as of 3.2, replaced by `ArrayFieldVector.ArrayFieldVector(FieldVector, FieldElement[])`
    `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(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[] v1, ArrayFieldVector<T> v2)` Deprecated. as of 3.2, replaced by `ArrayFieldVector.ArrayFieldVector(FieldElement[], FieldVector)`
    `ArrayFieldVector(T[] d, boolean copyArray)` Create a new ArrayFieldVector using the input array as the underlying data array.
    `ArrayFieldVector(T[] v1, FieldVector<T> v2)` Construct a vector by appending one vector to another vector.
    `ArrayFieldVector(T[] d, int pos, int size)` Construct a vector from part of a array.
    `ArrayFieldVector(T[] v1, T[] v2)` Construct a vector by appending one vector to another vector.
    `ArrayRealVector(ArrayRealVector v)` Construct a vector from another vector, using a deep copy.
    `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(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 and 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
    Constructor and 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
    Constructor and Description
    `MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer, int starts, RandomVectorGenerator generator)` 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 and 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
    Constructor and 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 and 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 and 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 and 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
    Constructor and 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 and 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
    Constructor and 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 and 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.
    `static void` | Percentile.`copy(Percentile source, Percentile dest)` Copies source to dest.

    Constructors in org.apache.commons.math3.stat.descriptive.rank that throw NullArgumentException
    Constructor and 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 and Description
    --- | ---
    `static void` | Product.`copy(Product source, Product 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.
    `static void` | Sum.`copy(Sum source, Sum dest)` Copies source to dest.

    Constructors in org.apache.commons.math3.stat.descriptive.summary that throw NullArgumentException
    Constructor and 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 and 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.
    `static double` | TestUtils.`chiSquareTest(long[][] counts)`
    `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 boolean` | TestUtils.`chiSquareTest(long[][] counts, double alpha)`
    `double` | TTest.`homoscedasticT(double[] sample1, double[] sample2)` Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
    `static double` | TestUtils.`homoscedasticT(double[] sample1, double[] sample2)`
    `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.`homoscedasticT(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.
    `static double` | TestUtils.`homoscedasticTTest(double[] sample1, double[] sample2)`
    `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.
    `static boolean` | TestUtils.`homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
    `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.`homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
    `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)`
    `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.`pairedT(double[] sample1, double[] sample2)`
    `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.
    `static double` | TestUtils.`pairedTTest(double[] sample1, double[] sample2)`
    `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 boolean` | TestUtils.`pairedTTest(double[] sample1, double[] sample2, double alpha)`
    `double` | TTest.`t(double[] sample1, double[] sample2)` Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
    `static double` | TestUtils.`t(double[] sample1, double[] sample2)`
    `double` | TTest.`t(double mu, double[] observed)` Computes a t statistic given observed values and a comparison constant.
    `static double` | TestUtils.`t(double mu, double[] observed)`
    `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`.
    `static double` | TestUtils.`t(double mu, StatisticalSummary sampleStats)`
    `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.`t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
    `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.
    `static double` | TestUtils.`tTest(double[] sample1, double[] sample2)`
    `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`.
    `static boolean` | TestUtils.`tTest(double[] sample1, double[] sample2, double 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`.
    `static double` | TestUtils.`tTest(double mu, double[] sample)`
    `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`.
    `static boolean` | TestUtils.`tTest(double mu, double[] sample, double alpha)`
    `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`.
    `static double` | TestUtils.`tTest(double mu, StatisticalSummary sampleStats)`
    `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`.
    `static boolean` | TestUtils.`tTest(double mu, StatisticalSummary sampleStats, double alpha)`
    `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.
    `static double` | TestUtils.`tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
    `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`.
    `static boolean` | TestUtils.`tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double 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 and 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
    Constructor and Description
    `Incrementor(int max, Incrementor.MaxCountExceededCallback cb)` Defines a maximal count and a callback method to be triggered at counter exhaustion.
    `ResizableDoubleArray(ResizableDoubleArray original)` Copy constructor.

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