# statsmodels 0.8.0

Statistical computations and models for Python

## 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/version0.8.html

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
• Mixed Linear Model with mixed effects and variance components
• GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
• GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
• Discrete models:
• Logit and Probit
• Multinomial logit (MNLogit)
• Poisson regression
• Negative Binomial regression
• 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
• Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
• Univariate time series analysis: AR, ARIMA
• Vector autoregressive models, VAR and structural VAR
• 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
• Nonparametric statistics: (Univariate) 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 and regression on order statistic
• Mediation analysis
• Principal Component Analysis with missing data
• I/O
• Tools for reading Stata .dta files into numpy arrays.
• 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 master 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/

Binaries can be installed in Anaconda

conda install statsmodels

Development snapshots are also available in Anaconda (infrequently updated)

conda install -c https://conda.binstar.org/statsmodels statsmodels

## Installing from sources

See INSTALL.txt for requirements or see the documentation

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

## License

Modified BSD (3-clause)

## Discussion and Development

Discussions take place on our mailing list.

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

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

File Type Py Version Uploaded on Size
Python Wheel cp27 2017-02-09 5MB
Python Wheel cp27 2017-02-09 5MB
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Python Wheel cp34 2017-02-09 5MB
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Python Wheel cp36 2017-02-09 5MB
Python Wheel cp36 2017-02-09 6MB
Source 2017-02-08 9MB