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,
|Author||Peter Carbonetto, Matthew Stephens, David Gerard|
|Date of publication||2017-03-24 05:37:09 UTC|
|Maintainer||Peter Carbonetto <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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