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

The documentation for the latest release is at

http://www.statsmodels.org/stable/

The documentation for the development version is at

http://www.statsmodels.org/dev/

Recent improvements are highlighted in the release notes

http://www.statsmodels.org/stable/release/version0.8.html

Backups of documentation are available at http://statsmodels.github.io/stable/ and http://statsmodels.github.io/dev/.

• Linear regression models:
  • Ordinary least squares
  • Generalized least squares
  • Weighted least squares
  • Least squares with autoregressive errors
  • Quantile regression
• Mixed Linear Model with mixed effects and variance components
• GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
• GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
• Discrete models:
  • Logit and Probit
  • Multinomial logit (MNLogit)
  • Poisson regresion
  • Negative Binomial regression
• RLM: Robust linear models with support for several M-estimators.
• Time Series Analysis: models for time series analysis
  • Complete StateSpace modeling framework
    • Seasonal ARIMA and ARIMAX models
    • VARMA and VARMAX models
    • Dynamic Factor models
  • Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
  • Univariate time series analysis: AR, ARIMA
  • Vector autoregressive models, VAR and structural VAR
  • Hypothesis tests for time series: unit root, cointegration and others
  • Descriptive statistics and process models for time series analysis
• Survival analysis:
  • Proportional hazards regression (Cox models)
  • Survivor function estimation (Kaplan-Meier)
  • Cumulative incidence function estimation
• Nonparametric statistics: (Univariate) kernel density estimators
• Datasets: Datasets used for examples and in testing
• Statistics: a wide range of statistical tests
  • diagnostics and specification tests
  • goodness-of-fit and normality tests
  • functions for multiple testing
  • various additional statistical tests
• Imputation with MICE and regression on order statistic
• Mediation analysis
• Principal Component Analysis with missing data
• I/O
  • Tools for reading Stata .dta files into numpy arrays.
  • 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". This covers among others
  • Generalized method of moments (GMM) estimators
  • Kernel regression
  • Various extensions to scipy.stats.distributions
  • Panel data models
  • Information theoretic measures

The master branch on GitHub is the most up to date code

https://www.github.com/statsmodels/statsmodels

Source download of release tags are available on GitHub

https://github.com/statsmodels/statsmodels/tags

Binaries and source distributions are available from PyPi

http://pypi.python.org/pypi/statsmodels/

Binaries can be installed in Anaconda

conda install statsmodels

Development snapshots are also available in Anaconda (infrequently updated)

conda install -c https://conda.binstar.org/statsmodels statsmodels

See INSTALL.txt for requirements or see the documentation

http://statsmodels.github.io/dev/install.html

Modified BSD (3-clause)

Discussions take place on our mailing list.

http://groups.google.com/group/pystatsmodels

We are very interested in feedback about usability and suggestions for improvements.

Bug reports can be submitted to the issue tracker at

https://github.com/statsmodels/statsmodels/issues