The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <doi:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics—estimated effects and standard errors—are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users.
| Version: | 2.0.5 |
| Depends: | R (≥ 3.1.0) |
| Imports: | assertthat, truncnorm, SQUAREM, doParallel, pscl, Rcpp (≥ 0.10.5), foreach, etrunct |
| LinkingTo: | Rcpp |
| Suggests: | testthat, roxygen2, covr |
| Enhances: | REBayes, Rmosek |
| Published: | 2016-12-27 |
| Author: | Matthew Stephens, Chaoxing Dai, Mengyin Lu, David Gerard, Nan Xiao, Peter Carbonetto |
| Maintainer: | Peter Carbonetto <pcarbo at uchicago.edu> |
| License: | GPL (≥ 3) |
| URL: | http://github.com/stephens999/ashr |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | ashr results |
| Reference manual: | ashr.pdf |
| Package source: | ashr_2.0.5.tar.gz |
| Windows binaries: | r-devel: ashr_2.0.5.zip, r-release: ashr_2.0.5.zip, r-oldrel: ashr_2.0.5.zip |
| OS X El Capitan binaries: | r-release: ashr_2.0.5.tgz |
| OS X Mavericks binaries: | r-oldrel: ashr_2.0.5.tgz |
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