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

Packages that use NullArgumentException
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.linear | Linear algebra support.
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.clustering | Clustering algorithms
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 and confidence interval construction.
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
`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
`double[]` | `HarmonicOscillator.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[]` | `Logistic.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.value(double x, double... param)` Computes the value of the harmonic oscillator 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`.
`double` | `Logistic.Parametric.value(double x, double... param)` Computes the value of the sigmoid at `x`.
`double` | `Gaussian.Parametric.value(double x, double... param)` Computes the value of the Gaussian at `x`.

Constructors in org.apache.commons.math3.analysis.function that throw NullArgumentException
`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
`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
`MultivariateFunction` | `MicrosphereInterpolator.interpolate(double[][] xval, double[] yval)` Computes an interpolating function for the data set.

Constructors in org.apache.commons.math3.analysis.interpolation that throw NullArgumentException
`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
`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.
`DerivativeStructure` | `PolynomialFunction.value(DerivativeStructure t)` Simple mathematical function.

Constructors in org.apache.commons.math3.analysis.polynomials that throw NullArgumentException
`PolynomialFunction(double[] c)` Construct a polynomial with the given coefficients.

Uses of NullArgumentException in org.apache.commons.math3.analysis.solvers

Methods in org.apache.commons.math3.analysis.solvers that throw NullArgumentException
`static double[]` | `UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)` This method attempts to find two values a and b satisfying `lowerBound <= a < initial < b <= upperBound` `f(a) * f(b) < 0` If f is continuous on `[a,b],` this means that `a` and `b` bracket a root of f.
`static double[]` | `UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)` This method attempts to find two values a and b satisfying `lowerBound <= a < initial < b <= upperBound` `f(a) * f(b) <= 0` If f is continuous on `[a,b],` this means that `a` and `b` bracket a root of f.
`static boolean` | `UnivariateSolverUtils.isBracketing(UnivariateFunction function, double lower, double upper)` Check whether the interval bounds bracket a root.
`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
`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
`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
`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
`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
`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
`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
`void` | `ListPopulation.setChromosomes(List<Chromosome> chromosomes)` Deprecated. use `ListPopulation.addChromosomes(Collection)` instead

Constructors in org.apache.commons.math3.genetics that throw NullArgumentException
`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.linear

Methods in org.apache.commons.math3.linear that throw NullArgumentException
`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.
`RealMatrix` | `AbstractRealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)` Gets a submatrix.
`RealMatrix` | `RealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)` Gets a submatrix.
`FieldMatrix<T>` | `FieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)` Get a submatrix.
`FieldMatrix<T>` | `AbstractFieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)` Get a submatrix.
`FieldVector<T>` | `ArrayFieldVector.mapAdd(T d)` Map an addition operation to each entry.
`FieldVector<T>` | `SparseFieldVector.mapAdd(T d)` Deprecated. 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)` Deprecated. 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)` Deprecated. 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)` Deprecated. 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)` Deprecated. 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)` Deprecated. 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)` Deprecated. 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)` Deprecated. Map a subtraction operation to each entry.
`FieldVector<T>` | `FieldVector.mapSubtractToSelf(T d)` Map a subtraction operation to each entry.
`void` | `Array2DRowRealMatrix.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` | `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` | `Array2DRowFieldMatrix.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` | `BlockFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)` Replace the submatrix starting at `(row, column)` using data in the input `subMatrix` array.
`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)` 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` | `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, RealLinearOperator m, RealVector b, RealVector x)` 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` | `IterativeLinearSolver.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)` 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` | `PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)` 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` | `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` | `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)` 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` | `PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, 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.
`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
`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)` Construct a vector by appending one vector to another vector.
`ArrayFieldVector(ArrayFieldVector<T> v, boolean deep)` Construct a vector from another vector.
`ArrayFieldVector(ArrayFieldVector<T> v1, T[] v2)` Construct a vector by appending one vector to another vector.
`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(T[] d)` Construct a vector from an array, copying the input array.
`ArrayFieldVector(T[] v1, ArrayFieldVector<T> v2)` Construct a vector by appending one vector to another vector.
`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, 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.
`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.

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

Constructors in org.apache.commons.math3.optim.nonlinear.scalar that throw NullArgumentException
`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
`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
`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
`StableRandomGenerator(RandomGenerator generator, double alpha, double beta)` Create a new generator.

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

Methods in org.apache.commons.math3.stat.clustering that throw NullArgumentException
`List<Cluster<T>>` | `DBSCANClusterer.cluster(Collection<T> points)` 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
`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
`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
`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
`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
`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
`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
`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
`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
`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.
`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.
`static double` | `TestUtils.chiSquare(long[][] counts)`
`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.chiSquareTest(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 boolean` | `TestUtils.chiSquareTest(long[][] counts, double alpha)`
`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.homoscedasticT(double[] sample1, double[] sample2)`
`double` | `TTest.homoscedasticT(double[] sample1, double[] sample2)` Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
`static double` | `TestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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)`
`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 boolean` | `TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
`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 double` | `TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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.
`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)`
`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 boolean` | `TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)`
`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)`
`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 mu, double[] observed)`
`double` | `TTest.t(double mu, double[] observed)` Computes a t statistic given observed values and a comparison constant.
`static double` | `TestUtils.t(double mu, StatisticalSummary sampleStats)`
`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(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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)`
`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 boolean` | `TestUtils.tTest(double[] sample1, double[] sample2, double alpha)`
`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 double` | `TestUtils.tTest(double mu, double[] sample)`
`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 boolean` | `TestUtils.tTest(double mu, double[] sample, double alpha)`
`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 double` | `TestUtils.tTest(double mu, StatisticalSummary sampleStats)`
`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 boolean` | `TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)`
`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 double` | `TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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 boolean` | `TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)`
`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
`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 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
`ResizableDoubleArray(ResizableDoubleArray original)` Copy constructor.

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