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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

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
Release History

Release History

This version
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0.8.0

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0.8.0rc1

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0.6.1

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0.6.0

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0.6.0-rc2

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0.6.0-rc1

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0.5.0

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0.5.0rc1

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0.4.3

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0.4.1

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0.4.0

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0.4.0rc2

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
statsmodels-0.8.0-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (5.4 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Feb 9, 2017
statsmodels-0.8.0-cp27-cp27m-manylinux1_x86_64.whl (6.2 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Feb 9, 2017
statsmodels-0.8.0-cp27-cp27mu-manylinux1_x86_64.whl (6.2 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Feb 9, 2017
statsmodels-0.8.0-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (5.4 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Feb 9, 2017
statsmodels-0.8.0-cp34-cp34m-manylinux1_x86_64.whl (6.2 MB) Copy SHA256 Checksum SHA256 cp34 Wheel Feb 9, 2017
statsmodels-0.8.0-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (5.4 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Feb 9, 2017
statsmodels-0.8.0-cp35-cp35m-manylinux1_x86_64.whl (6.2 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Feb 9, 2017
statsmodels-0.8.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (5.4 MB) Copy SHA256 Checksum SHA256 cp36 Wheel Feb 9, 2017
statsmodels-0.8.0-cp36-cp36m-manylinux1_x86_64.whl (6.3 MB) Copy SHA256 Checksum SHA256 cp36 Wheel Feb 9, 2017
statsmodels-0.8.0.tar.gz (9.5 MB) Copy SHA256 Checksum SHA256 Source Feb 8, 2017

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