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

- Packages that use NotStrictlyPositiveException
  Package | Description
  --- | ---
  org.apache.commons.math3.analysis | Parent package for common numerical analysis procedures, including root finding, function interpolation and integration.
  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.integration.gauss | Gauss family of quadrature schemes.
  org.apache.commons.math3.analysis.interpolation | Univariate real functions interpolation algorithms.
  org.apache.commons.math3.analysis.solvers | Root finding algorithms, for univariate real functions.
  org.apache.commons.math3.distribution | Implementations of common discrete and continuous distributions.
  org.apache.commons.math3.distribution.fitting | Fitting of parameters against distributions.
  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.scalar.noderiv | This package provides optimization algorithms that do not require derivatives.
  org.apache.commons.math3.optim.nonlinear.vector | Algorithms for optimizing a vector function.
  org.apache.commons.math3.optimization.direct | This package provides optimization algorithms that don't require derivatives.
  org.apache.commons.math3.random | Random number and random data generators.
  org.apache.commons.math3.stat.correlation | Correlations/Covariance computations.
  org.apache.commons.math3.stat.inference | Classes providing hypothesis testing.
  org.apache.commons.math3.stat.interval | Classes providing binomial proportion confidence interval construction.
  org.apache.commons.math3.transform | Implementations of transform methods, including Fast Fourier transforms.
  org.apache.commons.math3.util | Convenience routines and common data structures used throughout the commons-math library.

- - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis

    Methods in org.apache.commons.math3.analysis that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `static double[]` | FunctionUtils.`sample(UnivariateFunction f, double min, double max, int n)` Samples the specified univariate real function on the specified interval.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.function

    Methods in org.apache.commons.math3.analysis.function that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `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` | 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 NotStrictlyPositiveException
    Constructor and Description
    `Gaussian(double mean, double sigma)` Normalized gaussian with given mean and standard deviation.
    `Gaussian(double norm, double mean, double sigma)` Gaussian with given normalization factor, mean and standard deviation.
    `Logistic(double k, double m, double b, double q, double a, double n)`

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration

    Constructors in org.apache.commons.math3.analysis.integration that throw NotStrictlyPositiveException
    Constructor and Description
    `BaseAbstractUnivariateIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Construct an integrator with given accuracies and iteration counts.
    `BaseAbstractUnivariateIntegrator(int minimalIterationCount, int maximalIterationCount)` Construct an integrator with given iteration counts.
    `IterativeLegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy)` Builds an integrator with given accuracies.
    `IterativeLegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Builds an integrator with given accuracies and iterations counts.
    `IterativeLegendreGaussIntegrator(int n, int minimalIterationCount, int maximalIterationCount)` Builds an integrator with given iteration counts.
    `LegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Deprecated. Build a Legendre-Gauss integrator with given accuracies and iterations counts.
    `MidPointIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Build a midpoint integrator with given accuracies and iterations counts.
    `MidPointIntegrator(int minimalIterationCount, int maximalIterationCount)` Build a midpoint integrator with given iteration counts.
    `RombergIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Build a Romberg integrator with given accuracies and iterations counts.
    `RombergIntegrator(int minimalIterationCount, int maximalIterationCount)` Build a Romberg integrator with given iteration counts.
    `SimpsonIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Build a Simpson integrator with given accuracies and iterations counts.
    `SimpsonIntegrator(int minimalIterationCount, int maximalIterationCount)` Build a Simpson integrator with given iteration counts.
    `TrapezoidIntegrator(double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Build a trapezoid integrator with given accuracies and iterations counts.
    `TrapezoidIntegrator(int minimalIterationCount, int maximalIterationCount)` Build a trapezoid integrator with given iteration counts.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.integration.gauss

    Methods in org.apache.commons.math3.analysis.integration.gauss that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `Pair<double[],double[]>` | BaseRuleFactory.`getRule(int numberOfPoints)` Gets a copy of the quadrature rule with the given number of integration points.
    `GaussIntegrator` | GaussIntegratorFactory.`legendre(int numberOfPoints, double lowerBound, double upperBound)` Creates a Gauss-Legendre integrator of the given order.
    `GaussIntegrator` | GaussIntegratorFactory.`legendreHighPrecision(int numberOfPoints)` Creates a Gauss-Legendre integrator of the given order.
    `GaussIntegrator` | GaussIntegratorFactory.`legendreHighPrecision(int numberOfPoints, double lowerBound, double upperBound)` Creates an integrator of the given order, and whose call to the `integrate` method will perform an integration on the given interval.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.interpolation

    Constructors in org.apache.commons.math3.analysis.interpolation that throw NotStrictlyPositiveException
    Constructor and Description
    `MicrosphereInterpolator(int elements, int exponent)` Deprecated. Create a microsphere interpolator.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.analysis.solvers

    Methods in org.apache.commons.math3.analysis.solvers that throw NotStrictlyPositiveException
    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.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.distribution

    Methods in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `double[][]` | MultivariateRealDistribution.`sample(int sampleSize)` Generates a list of a random value vectors from the distribution.
    `Object[]` | EnumeratedDistribution.`sample(int sampleSize)` Generate a random sample from the distribution.
    `T[]` | EnumeratedDistribution.`sample(int sampleSize, T[] array)` Generate a random sample from the distribution.

    Constructors in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
    Constructor and Description
    `ExponentialDistribution(RandomGenerator rng, double mean)` Creates an exponential distribution.
    `ExponentialDistribution(RandomGenerator rng, double mean, double inverseCumAccuracy)` Creates an exponential distribution.
    `FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)` Creates an F distribution using the given degrees of freedom.
    `FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)` Creates an F distribution using the given degrees of freedom and inverse cumulative probability accuracy.
    `FDistribution(RandomGenerator rng, double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)` Creates an F distribution.
    `FDistribution(RandomGenerator rng, double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)` Creates an F distribution.
    `GammaDistribution(double shape, double scale)` Creates a new gamma distribution with specified values of the shape and scale parameters.
    `GammaDistribution(double shape, double scale, double inverseCumAccuracy)` Creates a new gamma distribution with specified values of the shape and scale parameters.
    `GammaDistribution(RandomGenerator rng, double shape, double scale)` Creates a Gamma distribution.
    `GammaDistribution(RandomGenerator rng, double shape, double scale, double inverseCumAccuracy)` Creates a Gamma distribution.
    `HypergeometricDistribution(int populationSize, int numberOfSuccesses, int sampleSize)` Construct a new hypergeometric distribution with the specified population size, number of successes in the population, and sample size.
    `HypergeometricDistribution(RandomGenerator rng, int populationSize, int numberOfSuccesses, int sampleSize)` Creates a new hypergeometric distribution.
    `KolmogorovSmirnovDistribution(int n)` Deprecated.
    `LogNormalDistribution(double scale, double shape)` Create a log-normal distribution using the specified scale and shape.
    `LogNormalDistribution(double scale, double shape, double inverseCumAccuracy)` Create a log-normal distribution using the specified scale, shape and inverse cumulative distribution accuracy.
    `LogNormalDistribution(RandomGenerator rng, double scale, double shape)` Creates a log-normal distribution.
    `LogNormalDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)` Creates a log-normal distribution.
    `NormalDistribution(double mean, double sd)` Create a normal distribution using the given mean and standard deviation.
    `NormalDistribution(double mean, double sd, double inverseCumAccuracy)` Create a normal distribution using the given mean, standard deviation and inverse cumulative distribution accuracy.
    `NormalDistribution(RandomGenerator rng, double mean, double sd)` Creates a normal distribution.
    `NormalDistribution(RandomGenerator rng, double mean, double sd, double inverseCumAccuracy)` Creates a normal distribution.
    `ParetoDistribution(double scale, double shape)` Create a Pareto distribution using the specified scale and shape.
    `ParetoDistribution(double scale, double shape, double inverseCumAccuracy)` Create a Pareto distribution using the specified scale, shape and inverse cumulative distribution accuracy.
    `ParetoDistribution(RandomGenerator rng, double scale, double shape)` Creates a Pareto distribution.
    `ParetoDistribution(RandomGenerator rng, double scale, double shape, double inverseCumAccuracy)` Creates a Pareto distribution.
    `PascalDistribution(int r, double p)` Create a Pascal distribution with the given number of successes and probability of success.
    `PascalDistribution(RandomGenerator rng, int r, double p)` Create a Pascal distribution with the given number of successes and probability of success.
    `PoissonDistribution(double p)` Creates a new Poisson distribution with specified mean.
    `PoissonDistribution(double p, double epsilon)` Creates a new Poisson distribution with the specified mean and convergence criterion.
    `PoissonDistribution(double p, double epsilon, int maxIterations)` Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.
    `PoissonDistribution(RandomGenerator rng, double p, double epsilon, int maxIterations)` Creates a new Poisson distribution with specified mean, convergence criterion and maximum number of iterations.
    `TDistribution(double degreesOfFreedom)` Create a t distribution using the given degrees of freedom.
    `TDistribution(double degreesOfFreedom, double inverseCumAccuracy)` Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy.
    `TDistribution(RandomGenerator rng, double degreesOfFreedom)` Creates a t distribution.
    `TDistribution(RandomGenerator rng, double degreesOfFreedom, double inverseCumAccuracy)` Creates a t distribution.
    `WeibullDistribution(double alpha, double beta)` Create a Weibull distribution with the given shape and scale and a location equal to zero.
    `WeibullDistribution(RandomGenerator rng, double alpha, double beta)` Creates a Weibull distribution.
    `WeibullDistribution(RandomGenerator rng, double alpha, double beta, double inverseCumAccuracy)` Creates a Weibull distribution.
    `ZipfDistribution(RandomGenerator rng, int numberOfElements, double exponent)` Creates a Zipf distribution.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.distribution.fitting

    Methods in org.apache.commons.math3.distribution.fitting that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `static MixtureMultivariateNormalDistribution` | MultivariateNormalMixtureExpectationMaximization.`estimate(double[][] data, int numComponents)` Helper method to create a multivariate normal mixture model which can be used to initialize `MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution)`.
    `void` | MultivariateNormalMixtureExpectationMaximization.`fit(MixtureMultivariateNormalDistribution initialMixture)` Fit a mixture model to the data supplied to the constructor.
    `void` | MultivariateNormalMixtureExpectationMaximization.`fit(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold)` Fit a mixture model to the data supplied to the constructor.

    Constructors in org.apache.commons.math3.distribution.fitting that throw NotStrictlyPositiveException
    Constructor and Description
    `MultivariateNormalMixtureExpectationMaximization(double[][] data)` Creates an object to fit a multivariate normal mixture model to data.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.genetics

    Constructors in org.apache.commons.math3.genetics that throw NotStrictlyPositiveException
    Constructor and Description
    `NPointCrossover(int crossoverPoints)` Creates a new `NPointCrossover` policy using the given number of points.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.linear

    Methods in org.apache.commons.math3.linear that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `FieldMatrix<T>` | Array2DRowFieldMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
    `RealMatrix` | DiagonalMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `BlockRealMatrix` | BlockRealMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `abstract RealMatrix` | AbstractRealMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `abstract FieldMatrix<T>` | AbstractFieldMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
    `RealMatrix` | RealMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `RealMatrix` | Array2DRowRealMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `FieldMatrix<T>` | BlockFieldMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.
    `OpenMapRealMatrix` | OpenMapRealMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.
    `FieldMatrix<T>` | FieldMatrix.`createMatrix(int rowDimension, int columnDimension)` Create a new FieldMatrix of the same type as the instance with the supplied row and column dimensions.

    Constructors in org.apache.commons.math3.linear that throw NotStrictlyPositiveException
    Constructor and Description
    `AbstractFieldMatrix(Field<T> field, int rowDimension, int columnDimension)` Create a new FieldMatrix with the supplied row and column dimensions.
    `AbstractRealMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix with the supplied row and column dimensions.
    `Array2DRowFieldMatrix(Field<T> field, int rowDimension, int columnDimension)` Create a new `FieldMatrix<T>` with the supplied row and column dimensions.
    `Array2DRowRealMatrix(int rowDimension, int columnDimension)` Create a new RealMatrix with the supplied row and column dimensions.
    `BlockFieldMatrix(Field<T> field, int rows, int columns)` Create a new matrix with the supplied row and column dimensions.
    `BlockFieldMatrix(int rows, int columns, T[][] blockData, boolean copyArray)` Create a new dense matrix copying entries from block layout data.
    `BlockRealMatrix(double[][] rawData)` Create a new dense matrix copying entries from raw layout data.
    `BlockRealMatrix(int rows, int columns)` Create a new matrix with the supplied row and column dimensions.
    `BlockRealMatrix(int rows, int columns, double[][] blockData, boolean copyArray)` Create a new dense matrix copying entries from block layout data.
    `DiagonalMatrix(int dimension)` Creates a matrix with the supplied dimension.
    `OpenMapRealMatrix(int rowDimension, int columnDimension)` Build a sparse matrix with the supplied row and column dimensions.

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

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

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.optim.nonlinear.scalar.noderiv

    Constructors in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that throw NotStrictlyPositiveException
    Constructor and Description
    `CMAESOptimizer.PopulationSize(int size)`

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

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

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.optimization.direct

    Constructors in org.apache.commons.math3.optimization.direct that throw NotStrictlyPositiveException
    Constructor and Description
    `CMAESOptimizer.PopulationSize(int size)`

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.random

    Methods in org.apache.commons.math3.random that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `double` | RandomDataGenerator.`nextExponential(double mean)` Generates a random value from the exponential distribution with specified mean.
    `double` | RandomData.`nextExponential(double mean)` Deprecated. Generates a random value from the exponential distribution with specified mean.
    `double` | RandomDataImpl.`nextExponential(double mean)` Deprecated. Generates a random value from the exponential distribution with specified mean.
    `double` | RandomDataGenerator.`nextF(double numeratorDf, double denominatorDf)` Generates a random value from the `F Distribution`.
    `double` | RandomDataImpl.`nextF(double numeratorDf, double denominatorDf)` Deprecated. Generates a random value from the `F Distribution`.
    `double` | RandomDataGenerator.`nextGamma(double shape, double scale)` Generates a random value from the `Gamma Distribution`.
    `double` | RandomDataImpl.`nextGamma(double shape, double scale)` Deprecated. Generates a random value from the `Gamma Distribution`.
    `double` | RandomDataGenerator.`nextGaussian(double mu, double sigma)` Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
    `double` | RandomData.`nextGaussian(double mu, double sigma)` Deprecated. Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
    `double` | RandomDataImpl.`nextGaussian(double mu, double sigma)` Deprecated. Generates a random value from the Normal (or Gaussian) distribution with specified mean and standard deviation.
    `String` | RandomDataGenerator.`nextHexString(int len)` Generates a random string of hex characters of length `len`.
    `String` | RandomData.`nextHexString(int len)` Deprecated. Generates a random string of hex characters of length `len`.
    `String` | RandomDataImpl.`nextHexString(int len)` Deprecated. Generates a random string of hex characters of length `len`.
    `int` | RandomDataGenerator.`nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)` Generates a random value from the `Hypergeometric Distribution`.
    `int` | RandomDataImpl.`nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)` Deprecated. Generates a random value from the `Hypergeometric Distribution`.
    `int` | RandomDataGenerator.`nextPascal(int r, double p)` Generates a random value from the `Pascal Distribution`.
    `int` | RandomDataImpl.`nextPascal(int r, double p)` Deprecated. Generates a random value from the `Pascal Distribution`.
    `int[]` | RandomDataGenerator.`nextPermutation(int n, int k)` Generates an integer array of length `k` whose entries are selected randomly, without repetition, from the integers `0, ..., n - 1` (inclusive).
    `int[]` | RandomData.`nextPermutation(int n, int k)` Deprecated. Generates an integer array of length `k` whose entries are selected randomly, without repetition, from the integers `0, ..., n - 1` (inclusive).
    `int[]` | RandomDataImpl.`nextPermutation(int n, int k)` Deprecated. Generates an integer array of length `k` whose entries are selected randomly, without repetition, from the integers `0, ..., n - 1` (inclusive).
    `long` | RandomDataGenerator.`nextPoisson(double mean)` Generates a random value from the Poisson distribution with the given mean.
    `long` | RandomData.`nextPoisson(double mean)` Deprecated. Generates a random value from the Poisson distribution with the given mean.
    `long` | RandomDataImpl.`nextPoisson(double mean)` Deprecated. Generates a random value from the Poisson distribution with the given mean.
    `Object[]` | RandomDataGenerator.`nextSample(Collection<?> c, int k)` Returns an array of `k` objects selected randomly from the Collection `c`.
    `Object[]` | RandomData.`nextSample(Collection<?> c, int k)` Deprecated. Returns an array of `k` objects selected randomly from the Collection `c`.
    `Object[]` | RandomDataImpl.`nextSample(Collection<?> c, int k)` Deprecated. Returns an array of `k` objects selected randomly from the Collection `c`.
    `String` | RandomDataGenerator.`nextSecureHexString(int len)` Generates a random string of hex characters from a secure random sequence.
    `String` | RandomData.`nextSecureHexString(int len)` Deprecated. Generates a random string of hex characters from a secure random sequence.
    `String` | RandomDataImpl.`nextSecureHexString(int len)` Deprecated. Generates a random string of hex characters from a secure random sequence.
    `double` | RandomDataGenerator.`nextT(double df)` Generates a random value from the `T Distribution`.
    `double` | RandomDataImpl.`nextT(double df)` Deprecated. Generates a random value from the `T Distribution`.
    `double` | RandomDataGenerator.`nextWeibull(double shape, double scale)` Generates a random value from the `Weibull Distribution`.
    `double` | RandomDataImpl.`nextWeibull(double shape, double scale)` Deprecated. Generates a random value from the `Weibull Distribution`.
    `int` | RandomDataGenerator.`nextZipf(int numberOfElements, double exponent)` Generates a random value from the `Zipf Distribution`.
    `int` | RandomDataImpl.`nextZipf(int numberOfElements, double exponent)` Deprecated. Generates a random value from the `Zipf Distribution`.

    Constructors in org.apache.commons.math3.random that throw NotStrictlyPositiveException
    Constructor and Description
    `SobolSequenceGenerator(int dimension, InputStream is)` Construct a new Sobol sequence generator for the given space dimension with direction vectors loaded from the given stream.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.stat.correlation

    Methods in org.apache.commons.math3.stat.correlation that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `protected RealMatrix` | Covariance.`computeCovarianceMatrix(double[][] data)` Create a covariance matrix from a rectangular array whose columns represent covariates.
    `protected RealMatrix` | Covariance.`computeCovarianceMatrix(double[][] data, boolean biasCorrected)` Compute a covariance matrix from a rectangular array whose columns represent covariates.

    Constructors in org.apache.commons.math3.stat.correlation that throw NotStrictlyPositiveException
    Constructor and Description
    `Covariance(double[][] data)` Create a Covariance matrix from a rectangular array whose columns represent covariates.
    `Covariance(double[][] data, boolean biasCorrected)` Create a Covariance matrix from a rectangular array whose columns represent covariates.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.stat.inference

    Methods in org.apache.commons.math3.stat.inference that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `static double` | TestUtils.`chiSquare(double[] expected, long[] observed)`
    `double` | ChiSquareTest.`chiSquare(double[] expected, long[] observed)` Computes the Chi-Square statistic comparing `observed` and `expected` frequency counts.
    `static double` | TestUtils.`chiSquareTest(double[] expected, long[] observed)`
    `double` | ChiSquareTest.`chiSquareTest(double[] expected, long[] observed)` Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing the `observed` frequency counts to those in the `expected` array.
    `static boolean` | TestUtils.`chiSquareTest(double[] expected, long[] observed, double alpha)`
    `boolean` | ChiSquareTest.`chiSquareTest(double[] expected, long[] observed, double alpha)` Performs a Chi-square goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level `alpha`.
    `static double` | TestUtils.`g(double[] expected, long[] observed)`
    `double` | GTest.`g(double[] expected, long[] observed)` Computes the G statistic for Goodness of Fit comparing `observed` and `expected` frequency counts.
    `static double` | TestUtils.`gTest(double[] expected, long[] observed)`
    `double` | GTest.`gTest(double[] expected, long[] observed)` Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the `observed` frequency counts to those in the `expected` array.
    `static boolean` | TestUtils.`gTest(double[] expected, long[] observed, double alpha)`
    `boolean` | GTest.`gTest(double[] expected, long[] observed, double alpha)` Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level `alpha`.
    `static double` | TestUtils.`gTestIntrinsic(double[] expected, long[] observed)`
    `double` | GTest.`gTestIntrinsic(double[] expected, long[] observed)` Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
    `protected double` | TTest.`homoscedasticTTest(double m1, double m2, double v1, double v2, double n1, double n2)` Computes p-value for 2-sided, 2-sample t-test, under the assumption of equal subpopulation variances.
    `protected double` | TTest.`tTest(double m1, double m2, double v1, double v2, double n1, double n2)` Computes p-value for 2-sided, 2-sample t-test.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.stat.interval

    Methods in org.apache.commons.math3.stat.interval that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `ConfidenceInterval` | BinomialConfidenceInterval.`createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel)` Create a confidence interval for the true probability of success of an unknown binomial distribution with the given observed number of trials, successes and confidence level.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.transform

    Methods in org.apache.commons.math3.transform that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `double[]` | RealTransformer.`transform(UnivariateFunction f, double min, double max, int n, TransformType type)` Returns the (forward, inverse) transform of the specified real function, sampled on the specified interval.

  - ### Uses of NotStrictlyPositiveException in org.apache.commons.math3.util

    Methods in org.apache.commons.math3.util that throw NotStrictlyPositiveException
    Modifier and Type | Method and Description
    --- | ---
    `static void` | MathArrays.`checkPositive(double[] in)` Check that all entries of the input array are strictly positive.

    Constructors in org.apache.commons.math3.util that throw NotStrictlyPositiveException
    Constructor and Description
    `MultidimensionalCounter(int... size)` Create a counter.

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