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: 2.0.0
Depends: R (≥ 3.1.0)
Imports: methods, stats, graphics, lattice, latticeExtra
Suggests: glmnet, qtl
Published: 2016-05-28
Author: Peter Carbonetto, Matthew Stephens
Maintainer: Peter Carbonetto <peter.carbonetto at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: varbvs citation info
CRAN checks: varbvs results


Reference manual: varbvs.pdf
Package source: varbvs_2.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: varbvs_2.0.0.tgz, r-oldrel: varbvs_2.0.0.tgz
Old sources: varbvs archive


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