Sparse modeling provides a mean selecting a small number of nonzero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis.
Package details 


Author  Marc Ratkovic and Dustin Tingley 
Date of publication  20160310 23:32:18 
Maintainer  Marc Ratkovic <[email protected]> 
License  GPL (>= 2) 
Version  1.2 
Package repository  View on CRAN 
Installation 
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