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).
