Package: varbvs
Encoding: UTF-8
Type: Package
Version: 2.0-8
Date: 2017-03-09
Title: Large-Scale Bayesian Variable Selection Using Variational
        Methods
Author: Peter Carbonetto, Matthew Stephens, David Gerard
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, stats, graphics, lattice, latticeExtra, Rcpp
Suggests: glmnet, qtl, knitr, testthat
License: GPL (>= 3)
NeedsCompilation: yes
LazyData: true
URL: http://github.com/pcarbo/varbvs
LinkingTo: Rcpp
VignetteBuilder: knitr
Packaged: 2017-03-23 21:37:02 UTC; pcarbo
Repository: CRAN
Date/Publication: 2017-03-24 05:37:09 UTC
Built: R 3.2.5; x86_64-w64-mingw32; 2017-04-20 06:47:24 UTC; windows
Archs: i386, x64
