Large-Scale Bayesian Variable Selection Using Variational Methods R package varbvs version 2.6-10 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. Software Depends: R (>= 3.1.0) Imports: methods, Matrix, stats, graphics, lattice, latticeExtra, Rcpp, nor1mix LinkingTo: Rcpp Suggests: curl, glmnet, qtl, knitr, rmarkdown, testthat Peter Carbonetto <peter.carbonetto@gmail.com> Comprehensive R Archive Network (CRAN) Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb] GPL (>= 3) 2023-05-31 application/tgz https://CRAN.R-project.org/package=varbvs doi:10.32614/CRAN.package.varbvs