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Statsmodels: statistical modeling and econometrics in Python

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Installation from sources

In the top directory (the same as the file you are reading now), just do:

python install

See INSTALL.txt for requirements or

For more information.

Release Notes


The statsmodels code was started by Jonathan Taylor and was formerly included
as part of scipy. It was taken up to be tested, corrected, and extended as part
of the Google Summer of Code 2009.

What it is

Statsmodels under the scikits namespace as scikits.statsmodels. Statsmodels is a
pure python package that requires numpy and scipy. It offers a convenient
interface for fitting parameterized statistical models with growing support
for displaying univariate and multivariate summary statistics, regression summaries,
and (postestimation) statistical tests.

Main Feautures

* regression: 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 choice models: Poisson, probit, logit, multinomial logit
* rlm: Robust linear models with support for several M-estimators.
* datasets: Datasets to be distributed and used for examples and in testing.
* PyDTA: Tools for reading Stata *.dta files into numpy arrays.

There is also a sandbox which contains code for generalized additive models
(untested), mixed effects models, cox proportional hazards model (both are
untested and still dependent on the nipy formula framework), generating
descriptive statistics, and printing table output to ascii, latex, and html.
There is also experimental code for systems of equations regression,
time series models, and information theoretic measures.  None of this code
is considered "production ready".

Where to get it

Development branches will be on LaunchPad. This is where to go to get the most
up to date code in the trunk branch. Experimental code will also be hosted here
in different branches and merged to trunk often.  We try to make sure that the
trunk code is always stable.

Source download of stable tags will be on SourceForge.




Simplified BSD


The official documentation is hosted on SourceForge.

The sphinx docs are currently undergoing a lot of work. They are not yet
comprehensive, but should get you started.

Our blog will continue to be updated as we make progress on the code.

Discussion and Development

All chatter will take place on the or scipy-user mailing list. We are very
interested in receiving feedback about usability, suggestions for improvements,
and bug reports via the mailing list or the bug tracker at

There is also a google group at

to discuss development and design issues that are deemed to be too specialized
for the scipy-dev/user list.
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