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:10.1214/12BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.
Package details 


Author  Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb] 
Maintainer  Peter Carbonetto <peter.carbonetto@gmail.com> 
License  GPL (>= 3) 
Version  2.516 
URL  http://github.com/pcarbo/varbvs 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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