ashr (2.2-39)

Methods for Adaptive Shrinkage, using Empirical Bayes.

https://github.com/stephens999/ashr
http://cran.r-project.org/web/packages/ashr

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 accomodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).

Maintainer: Peter Carbonetto
Author(s): 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]

License: GPL (>= 3)

Uses: assertthat, doParallel, etrunct, foreach, Matrix, mixsqp, pscl, Rcpp, SQUAREM, truncnorm, ggplot2, testthat, knitr, REBayes, rmarkdown
Reverse suggests: ncvreg
Reverse enhances: palasso

Released 4 months ago.