Module statrs::distribution [−][src]
Expand description
Defines common interfaces for interacting with statistical distributions and provides concrete implementations for a variety of distributions.
Structs
Implements the
Bernoulli
distribution which is a special case of the
Binomial
distribution where n = 1
(referenced Here)
Implements the Beta distribution
Implements the Binomial distribution
Implements the Categorical distribution, also known as the generalized Bernoulli or discrete distribution
Implements the Cauchy distribution, also known as the Lorentz distribution.
Implements the Chi distribution
Implements the Chi-squared distribution which is a special case of the Gamma distribution (referenced Here)
Implements the Dirac Delta distribution
Implements the Dirichlet distribution
Implements the Discrete Uniform distribution
Implements the Empirical Distribution
Implements the Fisher-Snedecor distribution also commonly known as the F-distribution
Implements the Gamma distribution
Implements the Geometric distribution
Implements the Hypergeometric distribution
Implements the Inverse Gamma distribution
Implements the Laplace distribution.
Implements the Log-normal distribution
Implements the Multinomial distribution which is a generalization of the Binomial distribution
Implements the Multivariate Normal distribution using the “nalgebra” crate for matrix operations
Implements the NegativeBinomial distribution
Implements the Normal distribution
Implements the Pareto distribution
Implements the Poisson distribution
Implements the Student’s T distribution
Implements the Triangular distribution
Implements the Continuous Uniform distribution
Implements the Weibull distribution
Traits
The Continuous
trait provides an interface for interacting with
continuous statistical distributions
The ContinuousCDF
trait is used to specify an interface for univariate
distributions for which cdf float arguments are sensible.
The Discrete
trait provides an interface for interacting with discrete
statistical distributions
The DiscreteCDF
trait is used to specify an interface for univariate
discrete distributions.