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


Info: This package contains files in non-standard labels.

conda install

  • linux-64  v2.2_32
  • osx-64  v2.2_32
  • win-64  v2.2_32
To install this package with conda run one of the following:
conda install -c conda-forge r-ashr
conda install -c conda-forge/label/gcc7 r-ashr
conda install -c conda-forge/label/cf201901 r-ashr


PRIVACY POLICY  |  EULA (Anaconda Cloud v2.33.29) © 2019 Anaconda, Inc. All Rights Reserved.