EMVS: The Expectation-Maximization Approach to Bayesian Variable Selection

An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization paths for linear regression. Rockova and George (2014) <doi:10.1080/01621459.2013.869223>.

Version: 1.0
Imports: Rcpp (≥ 0.12.16)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2018-04-24
Author: Veronika Rockova [aut,cre], Gemma Moran [aut]
Maintainer: Gemma Moran <gmoran at wharton.upenn.edu>
License: GPL-3
URL: https://doi.org/10.1080/01621459.2013.869223
NeedsCompilation: yes
CRAN checks: EMVS results

Downloads:

Reference manual: EMVS.pdf
Vignettes: EMVS Vignette
Package source: EMVS_1.0.tar.gz
Windows binaries: r-devel: EMVS_1.0.zip, r-devel-gcc8: EMVS_1.0.zip, r-release: EMVS_1.0.zip, r-oldrel: EMVS_1.0.zip
OS X binaries: r-release: EMVS_1.0.tgz, r-oldrel: EMVS_1.0.tgz

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