Module statrs::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 NegativeBinomial 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.