Py-statsmodels

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.