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
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:

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