ashr 2.2-32

Methods for Adaptive Shrinkage, using Empirical Bayes

Released Feb 22, 2019 by Peter Carbonetto

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etrunct 0.1 Matrix 1.2-15 truncnorm 1.0-8 Rcpp foreach 1.4.4 assertthat 0.2.0 doParallel 1.0.14 mixsqp 0.1-97 pscl 1.5.2 SQUAREM 2017.10-1

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).



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This package was last tested against Renjin 0.9.2724 on Feb 24, 2019.



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