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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 gmail.com>
BugReports: https://github.com/pcarbo/varbvs/issues
License: GPL (≥ 3)
URL: https://github.com/pcarbo/varbvs
NeedsCompilation: yes
Citation: varbvs citation info
CRAN checks: varbvs results

Documentation:

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

Downloads:

Package source: varbvs_2.6-10.tar.gz
Windows binaries: r-devel: varbvs_2.6-10.zip, r-release: varbvs_2.6-10.zip, r-oldrel: varbvs_2.6-10.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=varbvs to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.