DESCRIPTION

Package: varbvs
Encoding: UTF-8
Type: Package
Version: 2.6-8
Date: 2023-03-22
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