All Classes and Interfaces

Class
Description
Base class for probability distributions on the reals.
Base class for integer-valued discrete distributions.
Represents an alternative hypothesis for a hypothesis test.
Argument validation methods.
Utilities for argument validation.
Base implementation for the result of a test for significance.
Implementation of the beta distribution.
Represents the BigInteger result of a statistic computed over a set of values.
Implementation of the binomial distribution.
Implements binomial test statistics.
Provide an interval that brackets a local minimum of a function.
For a function defined on some interval (lo, hi), this class finds an approximation x to the point at which the function attains its minimum.
This class holds a point and the value of an objective function at this point.
Implementation of the Cauchy distribution.
Implementation of the chi-squared distribution.
Implements chi-square test statistics.
Constants for distribution calculations.
Represents an optional adjustment that is made when a discrete distribution is approximated by a continuous distribution.
Interface for distributions on the reals.
Distribution sampling functionality.
Represents an assumption on the dispersion of data.
Interface for distributions on the integers.
Distribution sampling functionality.
Package private exception class with constants for frequently used messages.
Represents a state object for computing a statistic over double valued input(s).
Statistics for double values.
A builder for DoubleStatistics.
Implementation of the exponential distribution.
Computes extended precision floating-point operations.
Implementation of the F-distribution.
Computes the first moment (arithmetic mean) using the definitional formula:
Implements Fisher's exact test.
Implementation of the folded normal distribution.
Specialisation for the half-normal distribution.
Regular implementation of the folded normal distribution.
Implementation of the gamma distribution.
Implementation of the geometric distribution.
Computes the geometric mean of the available values.
Implements G-test (Generalized Log-Likelihood Ratio Test) statistics.
Implementation of the Gumbel distribution.
Provide a wrapper around the HypergeometricDistribution that caches all probability mass values.
Implementation of the hypergeometric distribution.
Represents a non-equal comparison between two numbers.
Package private exception class with constants for frequently used messages.
A mutable 128-bit signed integer.
Support class for interpolation.
Support class for integer math.
Returns the maximum of the available values.
Computes the arithmetic mean of the available values.
Returns the minimum of the available values.
Computes the standard deviation of the available values.
Represents a state object for computing a statistic over int valued input(s).
Represents the int result of a statistic computed over a set of values.
Statistics for int values.
A builder for IntStatistics.
Returns the sum of the available values.
Returns the sum of the squares of the available values.
Computes the variance of the available values.
Computes the complementary probability for the one-sample Kolmogorov-Smirnov distribution.
Computes the complementary probability P[D_n^+ >= x] for the one-sided one-sample Kolmogorov-Smirnov distribution.
Defines a scaled power function.
Computes the complementary probability P[D_n >= x], or survival function (SF), for the two-sided one-sample Kolmogorov-Smirnov distribution.
Implements the Kolmogorov-Smirnov (K-S) test for equality of continuous distributions.
Result for the one-sample Kolmogorov-Smirnov test.
Result for the two-sample Kolmogorov-Smirnov test.
Computes the kurtosis of the available values.
Implementation of the Laplace distribution.
Implementation of the Lévy distribution.
Implementation of the logistic distribution.
Implementation of the log-normal distribution.
Implementation of the log-uniform distribution.
Returns the maximum of the available values.
Computes the arithmetic mean of the available values.
Returns the minimum of the available values.
Computes the standard deviation of the available values.
Represents a state object for computing a statistic over long valued input(s).
Represents the long result of a statistic computed over a set of values.
Statistics for long values.
A builder for LongStatistics.
Returns the sum of the available values.
Returns the sum of the squares of the available values.
Computes the variance of the available values.
Implements the Mann-Whitney U test (also called Wilcoxon rank-sum test).
Result for the Mann-Whitney U test.
Returns the maximum of the available values.
Computes the arithmetic mean of the available values.
Returns the median of the available values.
Returns the minimum of the available values.
Implementation of the Nakagami distribution.
Defines the policy for NaN values found in data.
Strategies for handling NaN values in rank transformations.
Defines a transformer for NaN values in arrays.
Support for creating NaNTransformer implementations.
A transformer that errors on NaN.
A transformer that moves NaN to the upper end of the array.
A NaN transformer that optionally copies the data.
Ranking based on the natural ordering on floating-point values.
Represents the position of a double value in a data array.
An expandable list of int values.
Implementation of the normal (Gaussian) distribution.
Implements one-way ANOVA (analysis of variance) statistics.
Result for the one-way ANOVA.
Implementation of the Pareto (Type I) distribution.
Implementation of the Pascal distribution.
Implementation of the Poisson distribution.
Returns the product of the available values.
Represents a method for computing a p-value for a test statistic.
Provides quantile computation.
Estimation methods for a quantile.
Interface representing a rank transformation.
Utility class used by various distributions to accurately compute their respective probability mass functions.
Search utility methods.
Contains the result of a test for significance.
Computes the skewness of the available values.
Provide support for square matrix basic algebraic operations.
Implementation of SquareMatrixSupport.RealSquareMatrix using a double[] array to store entries.
Define a real-valued square matrix.
Computes the standard deviation of the available values.
A statistic that can be computed on univariate data, for example a stream of double values.
A mutable result container that accumulates a StatisticResult.
Represents the result of a statistic computed over a set of values.
Utility methods for statistics.
Configuration for computation of statistics.
Utility computation methods.
Returns the sum of the available values.
Computes the sum of cubed deviations from the sample mean.
Computes the sum of fourth deviations from the sample mean.
Returns the sum of the natural logarithm of available values.
Computes the sum of squared deviations from the sample mean.
Returns the sum of the squares of the available values.
Implementation of Student's t-distribution.
Specialisation of the T-distribution used when there are infinite degrees of freedom.
Implementation of Student's T-distribution.
Strategies for handling tied values in rank transformations.
Implementation of the trapezoidal distribution.
Specialisation of the trapezoidal distribution used when the distribution simplifies to an alternative distribution.
Regular implementation of the trapezoidal distribution.
Specialisation of the trapezoidal distribution used when b == c.
Specialisation of the trapezoidal distribution used when a == b and c == d.
Implementation of the triangular distribution.
Implementation of the truncated normal distribution.
Implements Student's t-test statistics.
Result for the t-test.
A mutable 128-bit unsigned integer.
A mutable 192-bit unsigned integer.
A mutable 96-bit unsigned integer.
Implements an unconditioned exact test for a contingency table.
Compute the statistic for Boschloo's test.
A container of (key,value) pairs to store candidate minima.
Define the method to determine the more extreme tables.
Result for the unconditioned exact test.
An expandable list of (x,y) values.
Implementation of the uniform distribution.
Implementation of the uniform discrete distribution.
Computes the variance of the available values.
Implementation of the Weibull distribution.
Implements the Wilcoxon signed-rank test.
Result for the Wilcoxon signed-rank test.
Implementation of the Zipf distribution.