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: 2.6-10
Depends: R (≥ 3.1.0)
Imports: methods, Matrix, stats, graphics, lattice, latticeExtra, Rcpp, nor1mix
LinkingTo: Rcpp
Suggests: curl, glmnet, qtl, knitr, rmarkdown, testthat
Published: 2023-05-31
DOI: 10.32614/CRAN.package.varbvs
Author: Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]
Maintainer: Peter Carbonetto <peter.carbonetto at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: varbvs citation info
CRAN checks: varbvs results


Reference manual: varbvs.pdf
Vignettes: Crohn's disease demo
QTL mapping demo
Cytokine signaling genes demo
varbvs leukemia demo


Package source: varbvs_2.6-10.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): varbvs_2.6-10.tgz, r-oldrel (arm64): varbvs_2.6-10.tgz, r-release (x86_64): varbvs_2.6-10.tgz, r-oldrel (x86_64): varbvs_2.6-10.tgz
Old sources: varbvs archive

Reverse dependencies:

Reverse imports: SelectBoost


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