kstats-distributions/org.oremif.kstats.distributions

Package-level declarations

Types

class BernoulliDistribution(val probability: Double) : DiscreteDistribution

Represents the Bernoulli distribution, the simplest discrete probability distribution.

class BetaBinomialDistribution(val trials: Int, val alpha: Double, val beta: Double) : DiscreteDistribution

Represents the beta-binomial distribution, a compound distribution arising when the success probability of a binomial distribution is itself random and follows a beta distribution.

BetaDistribution

class BetaDistribution(val alpha: Double, val beta: Double) : ContinuousDistribution

Represents the Beta distribution, a continuous probability distribution defined on the interval 0, 1.

BinomialDistribution

class BinomialDistribution(val trials: Int, val probability: Double) : DiscreteDistribution

Represents the binomial distribution, defined by the number of trials and the probability of success on each trial.

CauchyDistribution

class CauchyDistribution(val location: Double = 0.0, val scale: Double = 1.0) : ContinuousDistribution

Represents the Cauchy distribution (also known as the Lorentz distribution).

ChiSquaredDistribution

class ChiSquaredDistribution(val degreesOfFreedom: Double) : ContinuousDistribution

Represents the chi-squared distribution, a continuous probability distribution defined on the interval [0, +infinity).

ContinuousDistribution

interface ContinuousDistribution : Distribution

Common interface for continuous probability distributions.

DiscreteDistribution

interface DiscreteDistribution : Distribution

Common interface for discrete probability distributions.

Distribution

sealed interface Distribution

Represents the common sealed interface for all probability distributions in kstats.

ExponentialDistribution

class ExponentialDistribution(val rate: Double = 1.0) : ContinuousDistribution

Represents the exponential distribution, a continuous probability distribution that models the time between events in a Poisson process.

FDistribution

class FDistribution(val dfNumerator: Double, val dfDenominator: Double) : ContinuousDistribution

Represents the F-distribution (also known as the Fisher-Snedecor distribution).

GammaDistribution

class GammaDistribution(val shape: Double, val rate: Double = 1.0) : ContinuousDistribution

Represents the Gamma distribution, a continuous probability distribution defined on the interval [0, +infinity).

GeometricDistribution

class GeometricDistribution(val probability: Double) : DiscreteDistribution

Represents the geometric distribution, which models the number of failures before the first success in a series of independent Bernoulli trials.

GumbelDistribution

class GumbelDistribution(val mu: Double = 0.0, val beta: Double = 1.0) : ContinuousDistribution

The Gumbel distribution (extreme value type I, right-skewed), defined by its location mu and scale beta parameters.

HypergeometricDistribution

class HypergeometricDistribution(val population: Int, val successes: Int, val draws: Int) : DiscreteDistribution

Represents the hypergeometric distribution, which models the number of successes when drawing without replacement from a finite population.

LaplaceDistribution

class LaplaceDistribution(val mu: Double = 0.0, val scale: Double = 1.0) : ContinuousDistribution

Represents the Laplace distribution (also known as the double exponential distribution), defined by its location mu and scale parameters.

LevyDistribution

class LevyDistribution(val mu: Double = 0.0, val c: Double = 1.0) : ContinuousDistribution

Represents the Levy distribution, a heavy-tailed, right-skewed continuous probability distribution supported on [mu, +infinity).

LogarithmicDistribution

class LogarithmicDistribution(val probability: Double) : DiscreteDistribution

Represents the logarithmic (log-series) distribution, a discrete power-series distribution on the positive integers {1, 2, 3, ...}.

LogisticDistribution

class LogisticDistribution(val mu: Double = 0.0, val scale: Double = 1.0) : ContinuousDistribution

The logistic distribution, defined by its location mu and scale parameter.

LogNormalDistribution

class LogNormalDistribution(val mu: Double = 0.0, val sigma: Double = 1.0) : ContinuousDistribution

Represents the log-normal distribution.

NakagamiDistribution

class NakagamiDistribution(val mu: Double, val omega: Double) : ContinuousDistribution

Represents the Nakagami-m distribution, a continuous probability distribution defined on the interval [0, +infinity).

NegativeBinomialDistribution

class NegativeBinomialDistribution(val successes: Int, val probability: Double) : DiscreteDistribution

Represents the negative binomial distribution, which models the number of failures before achieving a specified number of successes in a sequence of independent Bernoulli trials.

NormalDistribution

class NormalDistribution(val mu: Double = 0.0, val sigma: Double = 1.0) : ContinuousDistribution

Represents the normal (Gaussian) distribution, the most widely used continuous probability distribution in statistics.

ParetoDistribution

class ParetoDistribution(val shape: Double = 1.0, val scale: Double = 1.0) : ContinuousDistribution

The Pareto Type I distribution, defined by its shape (α) and scale (xm) parameters.

PoissonDistribution

class PoissonDistribution(val rate: Double) : DiscreteDistribution

Represents the Poisson distribution, defined by its average rate of occurrence.

StudentTDistribution

class StudentTDistribution(val degreesOfFreedom: Double) : ContinuousDistribution

Represents Student's t-distribution, a continuous probability distribution defined on the entire real line.

TriangularDistribution

class TriangularDistribution(val a: Double, val b: Double, val c: Double) : ContinuousDistribution

Represents the triangular distribution, a continuous probability distribution defined by a lower limit a, an upper limit b, and a mode (peak) c.

UniformDiscreteDistribution

class UniformDiscreteDistribution(val min: Int, val max: Int) : DiscreteDistribution

Represents the discrete uniform distribution, where all integer outcomes in a finite range are equally likely.

UniformDistribution

class UniformDistribution(val min: Double = 0.0, val max: Double = 1.0) : ContinuousDistribution

Represents the continuous uniform distribution, where all values in the interval [min, max] are equally likely.

WeibullDistribution

class WeibullDistribution(val shape: Double, val scale: Double = 1.0) : ContinuousDistribution

Represents the Weibull distribution.

ZipfDistribution

class ZipfDistribution(val numberOfElements: Int, val exponent: Double) : DiscreteDistribution

Represents the Zipf distribution (finite support variant), a discrete power-law distribution over the integers 1, 2, ..., numberOfElements.