.. 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 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