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 |
| Reference manual: | varbvs.html ,varbvs.pdf |
| Vignettes: | Crohn's disease demo (source,R code) QTL mapping demo (source,R code) Cytokine signaling genes demo (source,R code) varbvs leukemia demo (source,R code) |
| Package source: | varbvs_2.6-10.tar.gz |
| Windows binaries: | r-devel:varbvs_2.6-10.zip, r-release:varbvs_2.6-10.zip, r-oldrel:varbvs_2.6-10.zip |
| macOS binaries: | r-release (arm64):varbvs_2.6-10.tgz, r-oldrel (arm64):varbvs_2.6-10.tgz, r-release (x86_64):varbvs_2.6-10.tgz, r-oldrel (x86_64):varbvs_2.6-10.tgz |
| Old sources: | varbvs archive |
| Reverse imports: | SelectBoost |
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