ashr: 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. 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).
| Version: | 2.2-63 |
| 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 |
| Published: | 2023-08-21 |
| DOI: | 10.32614/CRAN.package.ashr |
| 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] |
| Maintainer: | Peter Carbonetto <pcarbo at uchicago.edu> |
| BugReports: | https://github.com/stephens999/ashr/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/stephens999/ashr |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| In views: | Bayesian |
| CRAN checks: | ashr results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: | mashr |
| Reverse imports: | cytoKernel,debrowser,DiffBind,dreamlet,ebnm,ldsep,limorhyde2,MixTwice,QTLExperiment,smashr |
| Reverse suggests: | BindingSiteFinder,colocboost,dar,DESeq2,flashier,ncvreg,palasso,ribosomeProfilingQC,topconfects |
Linking:
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