statsmodels ORG

@statsmodels ·statsmodels.sourceforge.net

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

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

- 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