statsmodels – statsmodels

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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

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