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# Introduction

## Background

Scipy.stats.models was originally written by Jonathan Taylor. For some time it was part of scipy but then removed from it. During the Google Summer of Code 2009, stats.models was corrected, tested and enhanced.

Now, we are releasing it as a standalone package in the scikits namespace as `scikits.statsmodels` to gain some experience with actual usage of it. This scikits.statsmodels is intended to be eventually re-included in scipy.

## Current Status

statsmodels is a pure python package.

statsmodels includes:

> - regression: mainly OLS and generalized least squares, GLS including weighted least squares and least squares with AR errors.
> - glm: generalized linear models
> - rlm: robust linear models
> - datasets: for examples and tests

The other code which we didn’t have enough time to verify and fix was moved to a sandbox folder. The formula framework is not used any more (in the verified code). Only the verified part would go into scipy.

Compared to the original code, the class structure and some of the method arguments have changed. Additional estimation results, e.g. test statistics have been included.

Most importantly, almost every result has been verified with at least one other statistical package: R, Stata or SAS. The guiding principal for the rewrite was that all numbers have to be verified, even if we don’t manage to cover everything. There are a few remaining issues, that we hope to clear up soon. Not all parts of the code have been tested for unexpected inputs and been made robust against them. Additionally, many of the tests call rpy to compare the results directly with R. We use an extended wrapper for R models in the test suite. This provides greater flexibility writing new test cases, but will eventually be replaced by hard coded expected results.

The code is written for plain NumPy arrays.

We have also included several datasets from the public domain and by permission for the tests and examples. The datasets follow fairly closely David Cournapeau’s datasets proposal in scikits.learn, with some small modifications. The datasets are set up so that it is easy to add more datasets.

## Looking Forward

We are distributing statsmodels as a standalone package to gain experience with the API, and to allow us to make changes without being committed to backwards compatibility. It will also give us the opportunity to find and kill some remaining bugs, and fill some holes in our test suite. However, statsmodels is intended to become part of scipy after some maturing of the package.

Earlier this summer, there was a discussion on the nipy mailing list on the structure of the API and about possible additional methods for the model classes. We would like to invite everyone to give statsmodels a test drive and report comments and possibilities for improvement and bugs to the scipy-user mailing list or file tickets on our bug tracker at https://bugs.launchpad.net/statsmodels

The development repository is at the new location http://code.launchpad.net/statsmodels, and the old location http://code.launchpad.net/~scipystats

We would also like to use statsmodels, or related projects to it, as a staging ground for new models. The current maintainers are mostly interested in econometrics and time series analysis, but we would like to invite any users or developers to contribute their own extensions to existing models or new models. statsmodels also contains some additional models in a sandbox folder. Those models were part of the original stats.models, but there was not enough time during GSOC 2009 to test, correct and refactor them. Any help to clean them up and bring them towards our testing standards would be very appreciated. Whether additional models are included in scipy or remain separate can be discussed as they mature.

statsmodels is distributed under the same license as scipy (BSD) so it can be readily integrated into scipy.

Josef Perktold and Skipper Seabold

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