scikits.statsmodels 0.2.0 release

While I find no time to blog, I thought I'd post our newest release announcement here.

We are happy to announce the 0.2.0 (beta) release of scikits.statsmodels. This is both a bug-fix and new feature release.

Download
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You can easy_install (or PyPI URL:
http://pypi.python.org/pypi/scikits.statsmodels/)

Source downloads: http://sourceforge.net/projects/statsmodels/

Development branches: http://code.launchpad.net/statsmodels

Note that the trunk branch on launchpad is almost always stable and has the most up to date changes since our releases are so few and far between.

Documentation
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http://statsmodels.sourceforge.net/

We invite you to install, kick the tires, and make bug reports and feature requests.

Feedback can either be on scipy-user or the mailing list at
http://groups.google.com/group/pystatsmodels?hl=en
Bug tracker: https://bugs.launchpad.net/statsmodels

Main Changes in 0.2.0
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* Improved documentation and expanded and more examples
* Added four discrete choice models: Poisson, Probit, Logit, and Multinomial Logit.
* Added PyDTA. Tools for reading Stata binary datasets (*.dta) and putting them into numpy arrays.
* Added four new datasets for examples and tests.
* Results classes have been refactored to use lazy evaluation.
* Improved support for maximum likelihood estimation.
* bugfixes
* renames for more consistency
RLM.fitted_values -> RLM.fittedvalues
GLMResults.resid_dev -> GLMResults.resid_deviance

Sandbox
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We are continuing to work on support for systems of equations models, panel data models, time series analysis, and information and entropy econometrics in the sandbox. This code is often merged into trunk as it becomes more robust.