Package: varbvs
Version: 2.0.0
Date: 2016-05-26
Title: Large-Scale Bayesian Variable Selection Using Variational
        Methods
Author: Peter Carbonetto, Matthew Stephens
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
Suggests: glmnet, qtl
License: GPL (>= 3)
NeedsCompilation: yes
URL: http://github.com/pcarbo/varbvs
Packaged: 2016-05-28 06:09:15 UTC; pcarbo
Repository: CRAN
Date/Publication: 2016-05-28 17:52:03
Built: R 3.3.2; x86_64-w64-mingw32; 2016-12-02 02:36:07 UTC; windows
Archs: i386, x64
