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

- Packages that use NullArgumentException
  Package | Description
  --- | ---
  org.apache.commons.math3.analysis.integration | Numerical integration (quadrature) algorithms for univariate real functions.
  org.apache.commons.math3.analysis.polynomials | Univariate real polynomials implementations, seen as differentiable univariate real functions.
  org.apache.commons.math3.complex | Complex number type and implementations of complex transcendental functions.
  org.apache.commons.math3.fraction | Fraction number type and fraction number formatting.
  org.apache.commons.math3.linear | Linear algebra support.
  org.apache.commons.math3.random | Random number and random data generators.
  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.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.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.

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

  - ### 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)`.
    `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)`.

  - ### 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.linear

    Methods in org.apache.commons.math3.linear that throw NullArgumentException
    Modifier and Type | Method and Description
    --- | ---
    `protected static void` | PreconditionedIterativeLinearSolver.`checkParameters(RealLinearOperator a, RealLinearOperator minv, 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.
    `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.
    `RealVector` | PreconditionedIterativeLinearSolver.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, 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 minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solve(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x)` 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)` 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` | 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.`solve(RealLinearOperator a, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.
    `abstract RealVector` | PreconditionedIterativeLinearSolver.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | ConjugateGradient.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x0)` Returns an estimate of the solution to the linear system A · x = b.
    `RealVector` | SymmLQ.`solveInPlace(RealLinearOperator a, RealLinearOperator minv, RealVector b, RealVector x, boolean goodb, double shift)` Returns an estimate of the solution to the linear system (A - shift · I) · 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.
    `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
    Constructor and Description
    `Array2DRowFieldMatrix(Field<T> field, 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.
    `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.random

    Methods in org.apache.commons.math3.random that throw NullArgumentException
    Modifier and Type | Method and Description
    --- | ---
    `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.

  - ### 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.

  - ### 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.

  - ### 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.

  - ### 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.

  - ### 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.
    `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)`
    `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` | ResizableDoubleArray.`copy(ResizableDoubleArray source, ResizableDoubleArray dest)` Copies source to dest, copying the underlying data, so dest is a new, independent copy of source.
    `double` | DefaultTransformer.`transform(Object o)`

    Constructors in org.apache.commons.math3.util that throw NullArgumentException
    Constructor and Description
    `ResizableDoubleArray(ResizableDoubleArray original)` Copy constructor.

Copyright © 2003-2012 The Apache Software Foundation. All Rights Reserved.