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