ashr

ashr-package


The main function in the ashr package is <code><a rd-options="" href="/link/ash?package=ashr&version=2.0.5" data-mini-rdoc="ashr::ash">ash</a></code>, which should be examined for more details. For simplicity only the most commonly-used options are documented under <code><a rd-options="" href="/link/ash?package=ashr&version=2.0.5" data-mini-rdoc="ashr::ash">ash</a></code>. For expert or interested users the documentation for function <code><a rd-options="" href="/link/ash.workhorse?package=ashr&version=2.0.5" data-mini-rdoc="ashr::ash.workhorse">ash.workhorse</a></code> provides documentation on all implemented options.


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.

Peter Carbonetto

Methods for Adaptive Shrinkage, using Empirical Bayes

ashr function


The main function in the ashr package is <code><a rd-options='' href='ash'>ash</a></code>, which should be examined for more details. For simplicity only the most commonly-used options are documented under <code><a rd-options='' href='ash'>ash</a></code>. For expert or interested users the documentation for function <code><a rd-options='' href='ash.workhorse'>ash.workhorse</a></code> provides documentation on all implemented options.


ashr: ashr

Description

Arguments