varbvs: Large-Scale Bayesian Variable Selection Using Variational Methods (original) (raw)
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: | 2.6-10 |
|---|---|
| Depends: | R (≥ 3.1.0) |
| Imports: | methods, Matrix, stats, graphics, lattice, latticeExtra, Rcpp, nor1mix |
| LinkingTo: | Rcpp |
| Suggests: | curl, glmnet, qtl, knitr, rmarkdown, testthat |
| Published: | 2023-05-31 |
| DOI: | 10.32614/CRAN.package.varbvs |
| Author: | Peter Carbonetto [aut, cre], Matthew Stephens [aut], David Gerard [ctb] |
| Maintainer: | Peter Carbonetto <peter.carbonetto at gmail.com> |
| BugReports: | https://github.com/pcarbo/varbvs/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/pcarbo/varbvs |
| NeedsCompilation: | yes |
| Citation: | varbvs citation info |
| CRAN checks: | varbvs results |
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