May 26, 2018

Complement to SciPy for statistical computations

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

Main Features

  • linear regression models GLS including WLS and LS aith AR errors and OLS.
  • glm Generalized linear models with support for all of the one-parameter exponential family distributions.
  • discrete regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators
  • rlm Robust linear models with support for several M-estimators.
  • tsa models for time series analysis - univariate AR, ARIMA; multivariate VAR and structural VAR
  • nonparametric Univariate kernel density estimators
  • datasets Datasets to be distributed and used for examples and in testing.
  • stats a wide range of statistical tests, diagnostics and specification tests
  • iolib Tools for reading Stata .dta files into numpy arrays, printing table output to ascii, latex, and html
  • miscellaneous models
  • sandbox statsmodels contains a sandbox folder with code in various stages of
  • developement and testing which is not considered “production ready”, including Mixed models, GARCH and GMM estimators, kernel regression, panel data models.

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