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, fastTopics, ldsep, limorhyde2, MixTwice, QTLExperiment |
| Reverse suggests: |
BindingSiteFinder, colocboost, dar, DESeq2, flashier, ncvreg, palasso, ribosomeProfilingQC, topconfects |
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