ashr: Methods for Adaptive Shrinkage, using Empirical Bayes
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 |
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Reverse dependencies:
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