varbvs (2.0-8)

Large-Scale Bayesian Variable Selection Using Variational Methods.

http://github.com/pcarbo/varbvs
http://cran.r-project.org/web/packages/varbvs

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, ). This software has been applied to large data sets with over a million variables and thousands of samples.

Maintainer: Peter Carbonetto
Author(s): Peter Carbonetto, Matthew Stephens, David Gerard

License: GPL (>= 3)

Uses: lattice, latticeExtra, Rcpp, qtl, glmnet, testthat, knitr

Released almost 3 years ago.