varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods

Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <doi:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.

Version: 2.5-16
Depends: R (≥ 3.1.0)
Imports: methods, Matrix, stats, graphics, lattice, latticeExtra, Rcpp, nor1mix
LinkingTo: Rcpp
Suggests: curl, glmnet, qtl, knitr, rmarkdown, testthat
Published: 2019-03-07
Author: Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto at gmail.com>
BugReports: http://github.com/pcarbo/varbvs/issues
License: GPL (≥ 3)
URL: http://github.com/pcarbo/varbvs
NeedsCompilation: yes
Citation: varbvs citation info
Materials: README
CRAN checks: varbvs results

Downloads:

Reference manual: varbvs.pdf
Vignettes: Crohn's disease demo
QTL mapping demo
Cytokine signaling genes demo
varbvs leukemia demo
Package source: varbvs_2.5-16.tar.gz
Windows binaries: r-devel: varbvs_2.5-16.zip, r-devel-gcc8: varbvs_2.5-16.zip, r-release: varbvs_2.5-16.zip, r-oldrel: varbvs_2.5-16.zip
OS X binaries: r-release: varbvs_2.5-16.tgz, r-oldrel: varbvs_2.5-16.tgz
Old sources: varbvs archive

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