| 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  functionpackage contains function objects that wrap the
      methods contained inMath, 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. | 
| 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. | 
| 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. | 
| 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) | 
| 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. | 
| 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
  integratemethod will perform an integration on the given interval. | 
| Constructor and Description | 
|---|
| MicrosphereInterpolator(int elements,
                       int exponent)Deprecated.  Create a microsphere interpolator. | 
| 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)withqandrset to 1.0 andmaximumIterationsset toInteger.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)withqandrset to 1.0. | 
| 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. | 
| 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. | 
| 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. | 
| Constructor and Description | 
|---|
| MultivariateNormalMixtureExpectationMaximization(double[][] data)Creates an object to fit a multivariate normal mixture model to data. | 
| Constructor and Description | 
|---|
| NPointCrossover(int crossoverPoints)Creates a new  NPointCrossoverpolicy using the given number of points. | 
| Modifier and Type | Method and Description | 
|---|---|
| FieldMatrix<T> | Array2DRowFieldMatrix. createMatrix(int rowDimension,
            int columnDimension)Create a new FieldMatrix | 
| 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 | 
| 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 | 
| 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 | 
| Constructor and Description | 
|---|
| AbstractFieldMatrix(Field<T> field,
                   int rowDimension,
                   int columnDimension)Create a new FieldMatrix | 
| 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. | 
| Constructor and Description | 
|---|
| MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer,
                               int starts,
                               RandomVectorGenerator generator)Create a multi-start optimizer from a single-start optimizer. | 
| Constructor and Description | 
|---|
| CMAESOptimizer.PopulationSize(int size) | 
| Constructor and Description | 
|---|
| MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer,
                                     int starts,
                                     RandomVectorGenerator generator)Deprecated.  Create a multi-start optimizer from a single-start optimizer. | 
| Constructor and Description | 
|---|
| CMAESOptimizer.PopulationSize(int size) | 
| 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  kwhose entries are selected
 randomly, without repetition, from the integers0, ..., n - 1(inclusive). | 
| int[] | RandomData. nextPermutation(int n,
               int k)Deprecated.  Generates an integer array of length  kwhose entries are selected
 randomly, without repetition, from the integers0, ..., n - 1(inclusive). | 
| int[] | RandomDataImpl. nextPermutation(int n,
               int k)Deprecated.  Generates an integer array of length  kwhose entries are selected
 randomly, without repetition, from the integers0, ..., 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  kobjects selected randomly from the
 Collectionc. | 
| Object[] | RandomData. nextSample(Collection<?> c,
          int k)Deprecated.  Returns an array of  kobjects selected randomly from the
 Collectionc. | 
| Object[] | RandomDataImpl. nextSample(Collection<?> c,
          int k)Deprecated.  Returns an array of  kobjects selected randomly from the
 Collectionc. | 
| 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. | 
| 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. | 
| 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. | 
| 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. | 
| Modifier and Type | Method and Description | 
|---|---|
| static double | TestUtils. chiSquare(double[] expected,
         long[] observed) | 
| double | ChiSquareTest. chiSquare(double[] expected,
         long[] observed) | 
| 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  observedfrequency counts to those in theexpectedarray. | 
| 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) | 
| 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
  observedfrequency counts to those in theexpectedarray. | 
| 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. | 
| 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. | 
| 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. | 
| Modifier and Type | Method and Description | 
|---|---|
| static void | MathArrays. checkPositive(double[] in)Check that all entries of the input array are strictly positive. | 
| Constructor and Description | 
|---|
| MultidimensionalCounter(int... size)Create a counter. | 
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