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

Downloads:

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

Reverse dependencies:

Reverse imports: CorShrink

Linking:

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