Sparse modeling provides a mean selecting a small number of non-zero 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.
Version: 1.2 Depends: R (≥ 3.0.2), MASS, ggplot2 Imports: Rcpp (≥ 0.11.0), msm, VGAM, MCMCpack, coda, glmnet, gridExtra, grid, GIGrvg LinkingTo: Rcpp, RcppArmadillo Published: 2016-03-10 DOI: 10.32614/CRAN.package.sparsereg Author: Marc Ratkovic and Dustin Tingley Maintainer: Marc Ratkovic <ratkovic at princeton.edu> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: yes Materials: ChangeLog CRAN checks: sparsereg results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sparsereg to link to this page.
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