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

- Packages that use NotStrictlyPositiveException
  Package | Description
  --- | ---
  org.apache.commons.math3.analysis.integration | Numerical integration (quadrature) algorithms for univariate real functions.
  org.apache.commons.math3.distribution | Implementations of common discrete and continuous distributions.
  org.apache.commons.math3.stat.inference | Classes providing hypothesis testing and confidence interval construction.

- - ### 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.
    `LegendreGaussIntegrator(int n, double relativeAccuracy, double absoluteAccuracy, int minimalIterationCount, int maximalIterationCount)` Build a Legendre-Gauss integrator with given accuracies and iterations 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.distribution

    Constructors in org.apache.commons.math3.distribution that throw NotStrictlyPositiveException
    Constructor and Description
    `ExponentialDistribution(double mean, double inverseCumAccuracy)` Create a exponential distribution with the given mean.
    `FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom)` Create a F distribution using the given degrees of freedom.
    `FDistribution(double numeratorDegreesOfFreedom, double denominatorDegreesOfFreedom, double inverseCumAccuracy)` Create an F distribution using the given degrees of freedom and inverse cumulative probability accuracy.
    `GammaDistribution(double alpha, double beta, double inverseCumAccuracy)` Create a new gamma distribution with the given `alpha` and `beta` values.
    `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.
    `KolmogorovSmirnovDistribution(int n)`
    `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.
    `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.
    `PascalDistribution(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.
    `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.
    `WeibullDistribution(double alpha, double beta)` Create a Weibull distribution with the given shape and scale and a location equal to zero.
    `WeibullDistribution(double alpha, double beta, double inverseCumAccuracy)` Create a Weibull distribution with the given shape, scale and inverse cumulative probability accuracy and a location equal to zero.
    `ZipfDistribution(int numberOfElements, double exponent)` Create a new Zipf distribution with the given number of elements and exponent.

  - ### 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
    --- | ---
    `double` | ChiSquareTest.`chiSquare(double[] expected, long[] observed)` Computes the Chi-Square statistic comparing `observed` and `expected` frequency counts.
    `static double` | TestUtils.`chiSquare(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 double` | TestUtils.`chiSquareTest(double[] expected, long[] observed)`
    `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 boolean` | TestUtils.`chiSquareTest(double[] expected, long[] observed, double alpha)`

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