Package: ashr Encoding: UTF-8 Type: Package Maintainer: Peter Carbonetto 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: 2.2-63 Date: 2023-08-21 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", . 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: 2023-08-21 18:44:14 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: 2023-08-21 23:50:03 UTC