Package: varbvs
Encoding: UTF-8
Type: Package
Version: 2.6-10
Date: 2023-05-31
Title: Large-Scale Bayesian Variable Selection Using Variational
        Methods
Authors@R: c(person("Peter","Carbonetto",role=c("aut","cre"),
                    email="peter.carbonetto@gmail.com"),
             person("Matthew","Stephens",role="aut"),
             person("David","Gerard",role="ctb"))
Maintainer: Peter Carbonetto <peter.carbonetto@gmail.com>
Description: 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.
Depends: R (>= 3.1.0)
Imports: methods, Matrix, stats, graphics, lattice, latticeExtra, Rcpp,
        nor1mix
Suggests: curl, glmnet, qtl, knitr, rmarkdown, testthat
License: GPL (>= 3)
NeedsCompilation: yes
LazyData: true
URL: https://github.com/pcarbo/varbvs
BugReports: https://github.com/pcarbo/varbvs/issues
LinkingTo: Rcpp
VignetteBuilder: knitr
Packaged: 2026-06-20 07:59:23 UTC; root
Author: Peter Carbonetto [aut, cre], Matthew Stephens [aut], David
        Gerard [ctb]
Config/pak/sysreqs: libjpeg-dev libpng-dev
Repository: https://pcarbo.r-universe.dev
Date/Publication: 2023-05-31 17:30:02 UTC
RemoteUrl: https://github.com/cran/varbvs
RemoteRef: HEAD
RemoteSha: 547e829ff36af1e22f26eadad4b3615ac9a9d062