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https://github.com/statsmodels/statsmodels

Statsmodels: statistical modeling and econometrics in Python
https://github.com/statsmodels/statsmodels

count-model data-analysis data-science econometrics forecasting generalized-linear-models hypothesis-testing prediction python regression-models robust-estimation statistics timeseries-analysis

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Statsmodels: statistical modeling and econometrics in Python

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README 

        
.. image:: docs/source/images/statsmodels-logo-v2-horizontal.svg

  :alt: Statsmodels logo



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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

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

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