S original by David W. Scott
R port by Albrecht Gebhardt <albrecht.gebhardt@aau.at>
adopted to recent S-PLUS by Stephen Kaluzny <spk@insightful.com>
David Scott's ASH routines ported from S-PLUS to R.

S original by David W. Scott
R port by Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>.

S original by David W. Scott
R port by Albrecht Gebhardt <albrecht.gebhardt@uni-klu.ac.at>

Matthew Stephens, Chaoxing Dai, Mengyin Lu, David Gerard, Nan Xiao, Peter Carbonetto
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.
assertthat, truncnorm, SQUAREM, doParallel, pscl, Rcpp (>=
0.10.5), foreach, etrunct

c(person("Matthew","Stephens",role="aut",
email="mstephens@uchicago.edu"),
person("Peter","Carbonetto",role=c("aut","cre"),
email="pcarbo@uchicago.edu"),
person("Chaoxing","Dai",role="ctb"),
person("David","Gerard",role="aut"),
person("Mengyin","Lu",role="aut"),
person("Lei","Sun",role="aut"),
person("Jason","Willwerscheid",role="aut"),
person("Nan","Xiao",role="aut"),
person("Mazon","Zeng",role="ctb"))
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).
Matrix, stats, graphics, assertthat, truncnorm, mixsqp,
SQUAREM, doParallel, pscl, Rcpp (>= 0.10.5), foreach, etrunct
testthat, knitr, rmarkdown, ggplot2, REBayes
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]