.. image:: docs/source/images/statsmodels-logo-v2-horizontal.svg
:alt: Statsmodels logo
|PyPI Version| |Conda Version| |License| |Azure CI Build Status|
|Codecov Coverage| |Coveralls Coverage| |PyPI downloads| |Conda downloads|
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/
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 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
* 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 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
Getting the latest code
=======================
Installing the most recent nightly wheel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The most recent nightly wheel can be installed using pip.
.. code:: bash
python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple statsmodels --upgrade --use-deprecated=legacy-resolver
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://www.statsmodels.org/stable/dev/test_notes
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
.. |Azure CI Build Status| image:: https://dev.azure.com/statsmodels/statsmodels-testing/_apis/build/status/statsmodels.statsmodels?branchName=main
:target: https://dev.azure.com/statsmodels/statsmodels-testing/_build/latest?definitionId=1&branchName=main
.. |Codecov Coverage| image:: https://codecov.io/gh/statsmodels/statsmodels/branch/main/graph/badge.svg
:target: https://codecov.io/gh/statsmodels/statsmodels
.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=main
:target: https://coveralls.io/github/statsmodels/statsmodels?branch=main
.. |PyPI downloads| image:: https://img.shields.io/pypi/dm/statsmodels?label=PyPI%20Downloads
:alt: PyPI - Downloads
:target: https://pypi.org/project/statsmodels/
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