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

https://www.statsmodels.org/stable/

The documentation for the development version is at

https://www.statsmodels.org/dev/

Recent improvements are highlighted in the release notes

https://www.statsmodels.org/stable/release/

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

- Linear regression models:
- Ordinary least squares
- Generalized least squares
- Weighted least squares
- Least squares with autoregressive errors
- Quantile regression
- Recursive least squares

- Mixed Linear Model with mixed effects and variance components
- GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
- Bayesian Mixed GLM for Binomial and Poisson
- GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
- Discrete models:
- Logit and Probit
- Multinomial logit (MNLogit)
- Poisson and Generalized Poisson regression
- Negative Binomial regression
- Zero-Inflated Count models

- 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
- Unobserved Component 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
- Vector error correction model, VECM
- exponential smoothing, Holt-Winters
- Hypothesis tests for time series: unit root, cointegration and others
- Descriptive statistics and process models for time series analysis

- Complete StateSpace modeling framework
- Survival analysis:
- Proportional hazards regression (Cox models)
- Survivor function estimation (Kaplan-Meier)
- Cumulative incidence function estimation

- Multivariate:
- Principal Component Analysis with missing data
- Factor Analysis with rotation
- MANOVA
- Canonical Correlation

- Nonparametric statistics: Univariate and multivariate 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, regression on order statistic and Gaussian imputation
- Mediation analysis
- Graphics includes plot functions for visual analysis of data and model results
- I/O
- Tools for reading Stata .dta files, but pandas has a more recent version
- Table output to ascii, latex, and html

- Miscellaneous models
- Sandbox: statsmodels contains a sandbox folder with code in various stages of
development 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 main 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

https://pypi.org/project/statsmodels/

Binaries can be installed in Anaconda

conda install statsmodels

See INSTALL.txt for requirements or see the documentation

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

Contributions in any form are welcome, including:

- Documentation improvements
- Additional tests
- New features to existing models
- New models

https://www.statsmodels.org/stable/dev/test_notes

for instructions on installing statsmodels in *editable* mode.

Modified BSD (3-clause)

Discussions take place on the mailing list

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

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

Bug reports can be submitted to the issue tracker at

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