Statistical computations and models for Python
Project description
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
The documentation for the development version is at
Recent improvements are highlighted in the release notes
Backups of documentation are available at http://statsmodels.github.io/stable/ and http://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 modle, 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 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 master branch on GitHub is the most up to date code
Source download of release tags are available on GitHub
Binaries and source distributions are available from PyPi
Binaries can be installed in Anaconda
conda install statsmodels
Installing from sources
See INSTALL.txt for requirements or see the documentation
License
Modified BSD (3-clause)
Discussion and Development
Discussions take place on our mailing list.
We are very interested in feedback about usability and suggestions for improvements.
Bug Reports
Bug reports can be submitted to the issue tracker at
Release history Release notifications | RSS feed
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