Skip to main content

Statistical computations and models for Python

About statsmodels

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

Documentation

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/version0.9.html

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

Main Features

• 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 modle, VECM

• exponential smoothing, Holt-Winters

• 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

• 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

How to get it

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

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

Binaries can be installed in Anaconda

conda install statsmodels

Installing from sources

See INSTALL.txt for requirements or see the documentation

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

Contributing

Contributions in any form are welcome, including:

• Documentation improvements

• Additional tests

• New features to existing models

• New models

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

for instructions on installing statsmodels in editable mode.

License

Modified BSD (3-clause)

Discussion and Development

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

Bug reports can be submitted to the issue tracker at

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

statsmodels-0.11.1.tar.gz (15.4 MB view hashes)

Uploaded Source

Built Distributions

statsmodels-0.11.1-cp38-none-win_amd64.whl (8.3 MB view hashes)

Uploaded CPython 3.8 Windows x86-64

statsmodels-0.11.1-cp38-none-win32.whl (7.8 MB view hashes)

Uploaded CPython 3.8 Windows x86

statsmodels-0.11.1-cp38-cp38-manylinux1_x86_64.whl (8.7 MB view hashes)

Uploaded CPython 3.8

statsmodels-0.11.1-cp38-cp38-manylinux1_i686.whl (8.2 MB view hashes)

Uploaded CPython 3.8

statsmodels-0.11.1-cp38-cp38-macosx_10_13_x86_64.whl (8.5 MB view hashes)

Uploaded CPython 3.8 macOS 10.13+ x86-64

statsmodels-0.11.1-cp37-none-win_amd64.whl (8.2 MB view hashes)

Uploaded CPython 3.7 Windows x86-64

statsmodels-0.11.1-cp37-none-win32.whl (7.7 MB view hashes)

Uploaded CPython 3.7 Windows x86

statsmodels-0.11.1-cp37-cp37m-manylinux1_x86_64.whl (8.7 MB view hashes)

Uploaded CPython 3.7m

statsmodels-0.11.1-cp37-cp37m-manylinux1_i686.whl (8.2 MB view hashes)

Uploaded CPython 3.7m

statsmodels-0.11.1-cp37-cp37m-macosx_10_13_x86_64.whl (8.4 MB view hashes)

Uploaded CPython 3.7m macOS 10.13+ x86-64

statsmodels-0.11.1-cp36-none-win_amd64.whl (8.2 MB view hashes)

Uploaded CPython 3.6 Windows x86-64

statsmodels-0.11.1-cp36-none-win32.whl (7.7 MB view hashes)

Uploaded CPython 3.6 Windows x86

statsmodels-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (8.7 MB view hashes)

Uploaded CPython 3.6m

statsmodels-0.11.1-cp36-cp36m-manylinux1_i686.whl (8.2 MB view hashes)

Uploaded CPython 3.6m

statsmodels-0.11.1-cp36-cp36m-macosx_10_13_x86_64.whl (8.4 MB view hashes)

Uploaded CPython 3.6m macOS 10.13+ x86-64

statsmodels-0.11.1-cp35-none-win_amd64.whl (8.1 MB view hashes)

Uploaded CPython 3.5 Windows x86-64

statsmodels-0.11.1-cp35-none-win32.whl (7.6 MB view hashes)

Uploaded CPython 3.5 Windows x86

statsmodels-0.11.1-cp35-cp35m-manylinux1_x86_64.whl (8.6 MB view hashes)

Uploaded CPython 3.5m

statsmodels-0.11.1-cp35-cp35m-manylinux1_i686.whl (8.2 MB view hashes)

Uploaded CPython 3.5m

statsmodels-0.11.1-cp35-cp35m-macosx_10_13_intel.whl (8.3 MB view hashes)

Uploaded CPython 3.5m macOS 10.13+ intel