Module distribution

Defines common interfaces for interacting with statistical distributions and provides concrete implementations for a variety of distributions.

Structs

Bernoulli

Implements the Bernoulli distribution which is a special case of the Binomial distribution where n = 1 (referenced Here)

Beta

Implements the Beta distribution

Binomial

Implements the Binomial distribution

Categorical

Implements the Categorical distribution, also known as the generalized Bernoulli or discrete distribution

Cauchy

Implements the Cauchy distribution, also known as the Lorentz distribution.

Chi

Implements the Chi distribution

ChiSquared

Implements the Chi-squared distribution which is a special case of the Gamma distribution (referenced Here)

Dirac

Implements the Dirac Delta distribution

Dirichlet

Implements the Dirichlet distribution

DiscreteUniform

Implements the Discrete Uniform distribution

Empirical

Implements the Empirical Distribution

Erlang

Implements the Erlang distribution which is a special case of the Gamma distribution

Exp

Implements the Exp distribution and is a special case of the Gamma distribution (referenced here)

FisherSnedecor

Implements the Fisher-Snedecor distribution also commonly known as the F-distribution

Gamma

Implements the Gamma distribution

Geometric

Implements the Geometric distribution

Hypergeometric

Implements the Hypergeometric distribution

InverseGamma

Implements the Inverse Gamma distribution

Laplace

Implements the Laplace distribution.

LogNormal

Implements the Log-normal distribution

Multinomial

Implements the Multinomial distribution which is a generalization of the Binomial distribution

MultivariateNormal

Implements the Multivariate Normal distribution using the “nalgebra” crate for matrix operations

NegativeBinomial

Implements the negative binomial distribution.

Normal

Implements the Normal distribution

Pareto

Implements the Pareto distribution

Poisson

Implements the Poisson distribution

StudentsT

Implements the Student’s T distribution

Triangular

Implements the Triangular distribution

Uniform

Implements the Continuous Uniform distribution

Weibull

Implements the Weibull distribution

Traits

Continuous

The Continuous trait provides an interface for interacting with continuous statistical distributions

ContinuousCDF

The ContinuousCDF trait is used to specify an interface for univariate distributions for which cdf float arguments are sensible.

Discrete

The Discrete trait provides an interface for interacting with discrete statistical distributions

DiscreteCDF

The DiscreteCDF trait is used to specify an interface for univariate discrete distributions.