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Selection of important and relevant variables to the analysis is one of the main aspects of the most of statistical analyses. This project does variable selection in a useful class of statistical models.
This project has been launched to fit mixture of expert models and simultaneously select important variables through regularization. The related statistical articles are
Khalili, A. and Chen, J. (2007). Variable Selection in Finite Mixture of Regression Models. Journal of the American Statistical Association, 102, 1025-1038.
Khalili, A. (2010). New Estimation and Feature Selection Methods in Mixture-of-Experts Models. The Canadian Journal of Statistics, 38, 519-539.
The current developers are Vahid Partovi Nia and Abbas Khalili.
The project summary page you can find here.