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

- Packages that use NumberIsTooSmallException
  Package | Description
  --- | ---
  org.apache.commons.math3.analysis.integration | Numerical integration (quadrature) algorithms for univariate real functions.
  org.apache.commons.math3.analysis.solvers | Root finding algorithms, for univariate real functions.
  org.apache.commons.math3.dfp | Decimal floating point library for Java
  org.apache.commons.math3.distribution | Implementations of common discrete and continuous distributions.
  org.apache.commons.math3.exception | Specialized exceptions for algorithms errors.
  org.apache.commons.math3.linear | Linear algebra support.
  org.apache.commons.math3.ode | This package provides classes to solve Ordinary Differential Equations problems.
  org.apache.commons.math3.ode.nonstiff | This package provides classes to solve non-stiff Ordinary Differential Equations problems.
  org.apache.commons.math3.stat.correlation | Correlations/Covariance computations.
  org.apache.commons.math3.stat.inference | Classes providing hypothesis testing and confidence interval construction.

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

    Constructors in org.apache.commons.math3.analysis.integration that throw NumberIsTooSmallException
    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 NumberIsTooSmallException in org.apache.commons.math3.analysis.solvers

    Constructors in org.apache.commons.math3.analysis.solvers that throw NumberIsTooSmallException
    Constructor and Description
    `BracketingNthOrderBrentSolver(double relativeAccuracy, double absoluteAccuracy, double functionValueAccuracy, int maximalOrder)` Construct a solver.
    `BracketingNthOrderBrentSolver(double relativeAccuracy, double absoluteAccuracy, int maximalOrder)` Construct a solver.
    `BracketingNthOrderBrentSolver(double absoluteAccuracy, int maximalOrder)` Construct a solver.

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

    Constructors in org.apache.commons.math3.dfp that throw NumberIsTooSmallException
    Constructor and Description
    `BracketingNthOrderBrentSolverDFP(Dfp relativeAccuracy, Dfp absoluteAccuracy, Dfp functionValueAccuracy, int maximalOrder)` Construct a solver.

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

    Constructors in org.apache.commons.math3.distribution that throw NumberIsTooSmallException
    Constructor and Description
    `TriangularDistribution(double a, double c, double b)` Create a triangular real distribution using the given lower limit, upper limit, and mode.

  - ### Uses of NumberIsTooSmallException in org.apache.commons.math3.exception

    Subclasses of NumberIsTooSmallException in org.apache.commons.math3.exception
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NotPositiveException` Exception to be thrown when the argument is negative.
    `class` | `NotStrictlyPositiveException` Exception to be thrown when the argument is negative.

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

    Subclasses of NumberIsTooSmallException in org.apache.commons.math3.linear
    Modifier and Type | Class and Description
    --- | ---
    `class` | `NonPositiveDefiniteMatrixException` Exception to be thrown when a positive definite matrix is expected.

  - ### Uses of NumberIsTooSmallException in org.apache.commons.math3.ode

    Methods in org.apache.commons.math3.ode that throw NumberIsTooSmallException
    Modifier and Type | Method and Description
    --- | ---
    `protected void` | AbstractIntegrator.`sanityChecks(ExpandableStatefulODE equations, double t)` Check the integration span.

  - ### Uses of NumberIsTooSmallException in org.apache.commons.math3.ode.nonstiff

    Methods in org.apache.commons.math3.ode.nonstiff that throw NumberIsTooSmallException
    Modifier and Type | Method and Description
    --- | ---
    `protected double` | AdaptiveStepsizeIntegrator.`filterStep(double h, boolean forward, boolean acceptSmall)` Filter the integration step.
    `protected void` | AdaptiveStepsizeIntegrator.`sanityChecks(ExpandableStatefulODE equations, double t)` Check the integration span.

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

    Methods in org.apache.commons.math3.stat.correlation that throw NumberIsTooSmallException
    Modifier and Type | Method and Description
    --- | ---
    `double` | StorelessCovariance.`getCovariance(int xIndex, int yIndex)` Get the covariance for an individual element of the covariance matrix.
    `RealMatrix` | StorelessCovariance.`getCovarianceMatrix()` Returns the covariance matrix
    `double[][]` | StorelessCovariance.`getData()` Return the covariance matrix as two-dimensional array.

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

    Methods in org.apache.commons.math3.stat.inference that throw NumberIsTooSmallException
    Modifier and Type | Method and Description
    --- | ---
    `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` | 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)`

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