Methods for Adaptive Shrinkage, using Empirical Bayes R package ashr version 2.2-63 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) 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). Software Depends: R (>= 3.1.0) Imports: Matrix, stats, graphics, Rcpp (>= 0.10.5), truncnorm, mixsqp, SQUAREM, etrunct, invgamma LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, ggplot2, REBayes Peter Carbonetto <pcarbo@uchicago.edu> Comprehensive R Archive Network (CRAN) 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] GPL (>= 3) 2023-08-21 application/tgz https://CRAN.R-project.org/package=ashr doi:10.32614/CRAN.package.ashr