Module distribution Copy item path

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

Dirichletnalgebra

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

Gumbel

Implements the Gumbel distribution, also known as the type-I generalized extreme value distribution.

Hypergeometric

Implements the Hypergeometric distribution

InverseGamma

Implements the Inverse Gamma distribution

Laplace

Implements the Laplace distribution.

LogNormal

Implements the Log-normal distribution

Multinomialnalgebra

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

MultivariateNormalnalgebra

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

MultivariateStudentnalgebra

Implements the Multivariate Student’s t-distribution 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

Enums§

BetaError

Represents the errors that can occur when creating a Beta.

BinomialError

Represents the errors that can occur when creating a Binomial.

CategoricalError

Represents the errors that can occur when creating a Categorical.

CauchyError

Represents the errors that can occur when creating a Cauchy.

ChiError

Represents the errors that can occur when creating a Chi.

DiracError

Represents the errors that can occur when creating a Dirac.

DirichletErrornalgebra

Represents the errors that can occur when creating a Dirichlet.

DiscreteUniformError

Represents the errors that can occur when creating a DiscreteUniform.

ExpError

Represents the errors that can occur when creating a Exp.

FisherSnedecorError

Represents the errors that can occur when creating a FisherSnedecor.

GammaError

Represents the errors that can occur when creating a Gamma.

GeometricError

Represents the errors that can occur when creating a Geometric.

GumbelError

Represents the errors that can occur when creating a Gumbel

HypergeometricError

Represents the errors that can occur when creating a Hypergeometric.

InverseGammaError

Represents the errors that can occur when creating an InverseGamma.

LaplaceError

Represents the errors that can occur when creating a Laplace.

LogNormalError

Represents the errors that can occur when creating a LogNormal.

MultinomialErrornalgebra

Represents the errors that can occur when creating a Multinomial.

MultivariateNormalErrornalgebra

Represents the errors that can occur when creating a MultivariateNormal.

MultivariateStudentErrornalgebra

Represents the errors that can occur when creating a MultivariateStudent.

NegativeBinomialError

Represents the errors that can occur when creating a NegativeBinomial.

NormalError

Represents the errors that can occur when creating a Normal.

ParetoError

Represents the errors that can occur when creating a Pareto.

PoissonError

Represents the errors that can occur when creating a Poisson.

StudentsTError

Represents the errors that can occur when creating a StudentsT.

TriangularError

Represents the errors that can occur when creating a Triangular.

UniformError

Represents the errors that can occur when creating a Uniform.

WeibullError

Represents the errors that can occur when creating a Weibull.

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.