statsmodels

https://github.com/quietcraftsmanship/statsmodels

Repository

README.rst

.. 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


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/version0.9.html

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

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

  developement and testing which is not considered "production ready".  This covers

  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


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

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


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

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


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

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


.. |Travis Build Status| image:: https://travis-ci.org/statsmodels/statsmodels.svg?branch=master
   :target: https://travis-ci.org/statsmodels/statsmodels
.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/gx18sd2wc63mfcuc/branch/master?svg=true
   :target: https://ci.appveyor.com/project/josef-pkt/statsmodels/branch/master
.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=master
   :target: https://coveralls.io/github/statsmodels/statsmodels?branch=master

.. |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/
.. |Conda downloads| image:: https://img.shields.io/conda/dn/conda-forge/statsmodels.svg?label=Conda%20downloads
   :target: https://anaconda.org/conda-forge/statsmodels/
.. |PyPI Version| image:: https://img.shields.io/pypi/v/statsmodels.svg
   :target: https://pypi.org/project/statsmodels/
.. |Conda Version| image:: https://anaconda.org/conda-forge/statsmodels/badges/version.svg
   :target: https://anaconda.org/conda-forge/statsmodels/
.. |License| image:: https://img.shields.io/pypi/l/statsmodels.svg
   :target: https://github.com/statsmodels/statsmodels/blob/main/LICENSE.txt

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
title: statsmodels
message: >-
  Please use following citation to cite statsmodels in
  scientific publications
type: software
authors:
  - given-names: Seabold
    family-names: Skipper
  - given-names: Perktold
    family-names: Josef
repository-code: 'https://github.com/statsmodels/statsmodels'
url: 'https://www.statsmodels.org/'
keywords:
  - python
  - data-science
  - statistics
  - prediction
  - econometrics
  - forecasting
  - data-analysis
  - regression-models
  - hypothesis-testing
  - generalized-linear-models
  - timeseries-analysis
  - robust-estimation
  - count-model
license: BSD-3-Clause
preferred-citation:
  type: article
  authors:
    - given-names: Seabold
      family-names: Skipper
    - given-names: Perktold
      family-names: Josef
  title: "statsmodels: Econometric and statistical modeling with python"
  journal: "9th Python in Science Conference"
  year: 2010

GitHub Events