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Statsmodels: statistical modeling and econometrics in Python

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DOC: Additions and fixes
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Octocat-spinner-32 docs DOC: Fix name collision
Octocat-spinner-32 examples Fixed examples and notebooks to be Python 2/3 compatible
Octocat-spinner-32 statsmodels DOC: Fix inline literals
Octocat-spinner-32 tools Further refinements to the compat module
Octocat-spinner-32 .bzrignore sandbox
Octocat-spinner-32 .coveragerc VAR test coverage
Octocat-spinner-32 .gitattributes add gitattributes (numpy minus .nsi)
Octocat-spinner-32 .gitignore Maint: gitignore: add dist (./dist doesn't work)
Octocat-spinner-32 .mailmap MAINT: Fix mailmap entry.
Octocat-spinner-32 .travis.orig.yml Changed build configurations to match those discusses in #1523
Octocat-spinner-32 .travis.yml Fix for conda 3.4 changes
Octocat-spinner-32 .travis_coveragerc Test coveralls issue removing if, then
Octocat-spinner-32 DOC: Point to changes in the docs.
Octocat-spinner-32 CONTRIBUTING.rst DOC: Ask for release notes and example.
Octocat-spinner-32 COPYRIGHTS.txt Docs: add license for qsturng-py and license notice for ordereddict
Octocat-spinner-32 INSTALL.txt DOC: Bump Cython version
Octocat-spinner-32 LICENSE.txt DOC: Update copyright dates
Octocat-spinner-32 BLD: try again, add test data file to
Octocat-spinner-32 README.txt DOC: very minor (trailing spaces) to trigger travis build
Octocat-spinner-32 README_l1.txt Updated examples
Octocat-spinner-32 build_bdists.bat BLD: Add scripts for building Python 3.4 on windows
Octocat-spinner-32 BLD: Add setuptools bootstrap script to version control
Octocat-spinner-32 setup.cfg changes to, add manifest to make sdist include data files wi…
Octocat-spinner-32 CLN: Refactored so that there is no longer a need for 2to3
What Statsmodels is
What it is

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

Main Features

* linear regression models: Generalized least squares (including weighted least squares and
  least squares with autoregressive errors), ordinary least squares.
* glm: Generalized linear models with support for all of the one-parameter
  exponential family distributions.
* discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators
* rlm: Robust linear models with support for several M-estimators.
* tsa: models for time series analysis
  - univariate time series analysis: AR, ARIMA
  - vector autoregressive models, VAR and structural VAR
  - descriptive statistics and process models for time series analysis
* nonparametric : (Univariate) kernel density estimators
* datasets: Datasets to be distributed and used for examples and in testing.
* stats: a wide range of statistical tests
  - diagnostics and specification tests
  - goodness-of-fit and normality tests
  - functions for multiple testing
  - various additional statistical tests
* iolib
  - Tools for reading Stata .dta files into numpy arrays.
  - printing 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 Mixed (repeated measures) Models, GARCH models, general method
  of moments (GMM) estimators, kernel regression, various extensions to scipy.stats.distributions,
  panel data models, generalized additive models and information theoretic measures.

Where 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

Installation from sources

See INSTALL.txt for requirements or see the documentation


Modified BSD (3-clause)


The official documentation is hosted on SourceForge

Windows Help
The source distribution for Windows includes a htmlhelp file (statsmodels.chm).
This can be opened from the python interpreter ::

    >>> import statsmodels.api as sm
    >>> sm.open_help()

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
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