varbvs (2.5-16)

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Large-Scale Bayesian Variable Selection Using Variational Methods.

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 [aut, cre], Matthew Stephens [aut], David Gerard [ctb]

License: GPL (>= 3)

Uses: lattice, latticeExtra, Matrix, nor1mix, Rcpp, qtl, glmnet, testthat, knitr, rmarkdown, curl

Released 12 months ago.

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