Package: ashr
Maintainer: Peter Carbonetto <pcarbo@uchicago.edu>
Author: Matthew Stephens, Chaoxing Dai, Mengyin Lu, David Gerard, Nan Xiao, Peter Carbonetto
Version: 2.0.5
Date: 2016-12-15
Title: Methods for Adaptive Shrinkage, using Empirical Bayes
Description: 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.
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
License: GPL (>= 3)
NeedsCompilation: yes
URL: http://github.com/stephens999/ashr
RoxygenNote: 5.0.1
Packaged: 2016-12-27 13:56:26 UTC; hornik
Repository: CRAN
Date/Publication: 2016-12-27 15:28:19
Built: R 3.3.2; x86_64-w64-mingw32; 2017-08-29 17:47:46 UTC; windows
Archs: x64
