sparsereg: Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data

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
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

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Reference manual: sparsereg.pdf
Package source: sparsereg_1.2.tar.gz
Windows binaries: r-devel: sparsereg_1.2.zip, r-release: sparsereg_1.2.zip, r-oldrel: sparsereg_1.2.zip
OS X Mavericks binaries: r-release: sparsereg_1.2.tgz, r-oldrel: sparsereg_1.2.tgz
Old sources: sparsereg archive

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