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. The ash() and ash.workhorse() also provides a flexible modeling interface that can accomodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).

Version: 2.2-7
Depends: R (≥ 3.1.0)
Imports: Matrix, stats, graphics, assertthat, truncnorm, SQUAREM, doParallel, pscl, Rcpp (≥ 0.10.5), foreach, etrunct
LinkingTo: Rcpp
Suggests: testthat, roxygen2, covr, knitr, rmarkdown, ggplot2, REBayes, Rmosek
Published: 2018-03-01
Author: Matthew Stephens [aut], Peter Carbonetto [aut, cre], Chaoxing Dai [ctb], David Gerard [aut], Mengyin Lu [aut], Lei Sun [aut], Jason Willwerscheid [aut], Nan Xiao [aut], Mazon Zeng [ctb]
Maintainer: Peter Carbonetto <pcarbo at uchicago.edu>
BugReports: https://github.com/stephens999/ashr/issues
License: GPL (≥ 3)
URL: https://github.com/stephens999/ashr
NeedsCompilation: yes
Materials: NEWS
CRAN checks: ashr results

Downloads:

Reference manual: ashr.pdf
Vignettes: Illustration of Adaptive Shrinkage
Package source: ashr_2.2-7.tar.gz
Windows binaries: r-devel: ashr_2.2-7.zip, r-release: ashr_2.2-7.zip, r-oldrel: ashr_2.2-7.zip
OS X binaries: r-release: ashr_2.2-7.tgz, r-oldrel: ashr_2.2-7.tgz
Old sources: ashr archive

Reverse dependencies:

Reverse imports: CorShrink

Linking:

Please use the canonical form https://CRAN.R-project.org/package=ashr to link to this page.