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.
|Author||Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb]|
|Maintainer||Peter Carbonetto <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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