Package: ashr
Encoding: UTF-8
Type: Package
Maintainer: Peter Carbonetto <pcarbo@uchicago.edu>
Authors@R: 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"))
Version: 2.2-54
Date: 2022-02-22
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. The
    ash() and ash.workhorse() also provides a flexible modeling
    interface that can accommodate a variety of likelihoods (e.g.,
    normal, Poisson) and mixture priors (e.g., uniform, normal).
Depends: R (>= 3.1.0)
Imports: Matrix, stats, graphics, Rcpp (>= 0.10.5), truncnorm, mixsqp,
        SQUAREM, etrunct, invgamma
Suggests: testthat, knitr, rmarkdown, ggplot2, REBayes
LinkingTo: Rcpp
License: GPL (>= 3)
NeedsCompilation: yes
URL: https://github.com/stephens999/ashr
BugReports: https://github.com/stephens999/ashr/issues
VignetteBuilder: knitr
RoxygenNote: 7.1.2
Packaged: 2022-02-22 14:28:37 UTC; pcarbo
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]
Repository: CRAN
Date/Publication: 2022-02-22 16:10:07 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-02-22 20:47:54 UTC; windows
Archs: x64
