The main estimators currently included in the code are generalized least squares, ordinary least squares, weighted least squares, autoregressive AR(p), generalized linear models (with several available distribution families and corresponding link functions), robust linear models, general additive models, and mixed effects models. The test coverage is starting to look pretty good, then there is just squashing the few remaining bugs and improving the postestimation statistics.
Some enhancements have also been made to the code. I have started to include some public domain or appropriately copyrighted datasets for testing purposes that could also be useful for examples and tutorials, so that every usage example doesn't have to start with generating your own random data. I have followed pretty closely to the datasets proposal in the Scikits Learn package.
We have also decided to break from the formula framework that is used in NiPy. It was in flux (being changed to take advantage of SymPy the last I heard) and is intended to be somewhat similar to the formula framework in R. In its place for now, I have written some convenience functions to append a constant to a design matrix or to handle categorical variables for estimation. For the moment, a typical model/estimator is used as
In : from models.regression import OLS
In : from models.datasets.longley.data import load
In : from models.functions import add_constant
In : data = load()
In : data.exog = add_constant(data.exog)
In : model = OLS(data.endog, data.exog)
In : results = model.fit()
In : results.params
array([ 1.50618723e+01, -3.58191793e-02, -2.02022980e+00,
-1.03322687e+00, -5.11041057e-02, 1.82915146e+03,
Barring any unforeseen difficulties, the models code should be available as a standalone package shortly after the midterm evaluation rapidly approaching in ten days. The second half of the summer will then be focused on optimizing the code, finalizing design issues, extending the models, and writing good documentation and tutorials so that the code can be included in SciPy!