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.
| Version: | 2.0.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | methods, stats, graphics, lattice, latticeExtra |
| Suggests: | glmnet, qtl |
| Published: | 2016-05-28 |
| Author: | Peter Carbonetto, Matthew Stephens |
| Maintainer: | Peter Carbonetto <peter.carbonetto at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | http://github.com/pcarbo/varbvs |
| NeedsCompilation: | yes |
| Citation: | varbvs citation info |
| CRAN checks: | varbvs results |
| Reference manual: | varbvs.pdf |
| Package source: | varbvs_2.0.0.tar.gz |
| Windows binaries: | r-devel: varbvs_2.0.0.zip, r-release: varbvs_2.0.0.zip, r-oldrel: varbvs_2.0.0.zip |
| OS X Mavericks binaries: | r-release: varbvs_2.0.0.tgz, r-oldrel: varbvs_2.0.0.tgz |
| Old sources: | varbvs archive |
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