.. module:: statsmodels.sandbox.distributions :synopsis: Probability distributions

.. currentmodule:: statsmodels.sandbox.distributions

.. _distributions:

Distributions

This section collects various additional functions and methods for statistical distributions.

Empirical Distributions

.. module:: statsmodels.distributions.empirical_distribution :synopsis: Tools for working with empirical distributions

.. currentmodule:: statsmodels.distributions.empirical_distribution

.. autosummary:: :toctree: generated/

ECDF ECDFDiscrete StepFunction monotone_fn_inverter

Count Distributions

The discrete module contains classes for count distributions that are based on discretizing a continuous distribution, and specific count distributions that are not available in scipy.distributions like generalized poisson and zero-inflated count models.

The latter are mainly in support of the corresponding models in statsmodels.discrete. Some methods are not specifically implemented and will use potentially slow inherited generic methods.

.. module:: statsmodels.distributions.discrete :synopsis: Support for count distributions

.. currentmodule:: statsmodels.distributions.discrete

.. autosummary:: :toctree: generated/

DiscretizedCount DiscretizedModel genpoisson_p zigenpoisson zinegbin zipoisson

Copula

The copula sub-module provides classes to model the dependence between parameters. Copulae are used to construct a multivariate joint distribution and provide a set of functions like sampling, PDF, CDF.

.. module:: statsmodels.distributions.copula.api :synopsis: Copula for modeling parameter dependence

.. currentmodule:: statsmodels.distributions.copula.api

.. autosummary:: :toctree: generated/

CopulaDistribution ArchimedeanCopula FrankCopula ClaytonCopula GumbelCopula GaussianCopula StudentTCopula ExtremeValueCopula IndependenceCopula

Distribution Extras

.. module:: statsmodels.sandbox.distributions.extras :synopsis: Probability distributions and random number generators

.. currentmodule:: statsmodels.sandbox.distributions.extras

Skew Distributions

.. autosummary:: :toctree: generated/

SkewNorm_gen SkewNorm2_gen ACSkewT_gen skewnorm2

Distributions based on Gram-Charlier expansion

.. autosummary:: :toctree: generated/

pdf_moments_st pdf_mvsk pdf_moments NormExpan_gen

cdf of multivariate normal wrapper for scipy.stats

.. autosummary:: :toctree: generated/

mvstdnormcdf mvnormcdf

Univariate Distributions by non-linear Transformations

Univariate distributions can be generated from a non-linear transformation of an existing univariate distribution. Transf_gen is a class that can generate a new distribution from a monotonic transformation, TransfTwo_gen can use hump-shaped or u-shaped transformation, such as abs or square. The remaining objects are special cases.

.. module:: statsmodels.sandbox.distributions.transformed :synopsis: Experimental probability distributions and random number generators

.. currentmodule:: statsmodels.sandbox.distributions.transformed

.. autosummary:: :toctree: generated/

TransfTwo_gen Transf_gen

ExpTransf_gen LogTransf_gen SquareFunc

absnormalg invdnormalg

loggammaexpg lognormalg negsquarenormalg

squarenormalg squaretg

Helper Functions

.. module:: statsmodels.tools.rng_qrng :synopsis: Tools for working with random variable generation

.. currentmodule:: statsmodels.tools.rng_qrng

.. autosummary:: :toctree: generated/

check_random_state