The models module of scipy.stats was originally written by Jonathan Taylor. For some time it was part of scipy but was later removed. During the Google Summer of Code 2009, statsmodels was corrected, tested, improved and released as a new package. Since then, the statsmodels development team has continued to add new models, plotting tools, and statistical methods.
Most results have been verified with at least one other statistical package: R, Stata or SAS. The guiding principal for the initial rewrite and for continued development is that all numbers have to be verified. Some statistical methods are tested with Monte Carlo studies. While we strive to follow this test driven approach, there is no guarantee that the code is bug-free and always works. Some auxiliary function are still insufficiently tested, some edge cases might not be correctly taken into account, and the possibility of numerical problems is inherent to many of the statistical models. We especially appreciate any help and reports for these kind of problems so we can keep improving the existing models.
The existing models are mostly settled in their user interface and we do not expect many large changes going forward. For the existing code, although there is no guarantee yet on API stability, we have long deprecation periods in all but very special cases, and we try to keep changes that require adjustments by existing users to a minimal level. For newer models we might adjust the user interface as we gain more experience and obtain feedback. These changes will always be noted in our release notes available in the documentation.
We are grateful for the financial support that we obtained for the development of statsmodels:
Google www.google.com : Google Summer of Code (GSOC) 2009-2013.
AQR www.aqr.com : financial sponsor for the work on Vector Autoregressive Models (VAR) by Wes McKinney
We would also like to thank our hosting providers, github for the public code repository, sourceforge for hosting our documentation and python.org for making our downloads available on PyPi.