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).

Methods for Adaptive Shrinkage, using Empirical Bayes

package-ubuntu-20.04-r-4.0.5#cran#ashr-2.2-47

<p>prunes out mixture components with low weight
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>m</code></td>
<td>
<p>What is this argument?</p>
</td>
</tr>
<tr valign="top">
<td><code>thresh</code></td>
<td>
<p>the threshold below which components are removed</p>
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#ashr-2.2-47#docs#prune

prune

Description

Usage

Arguments

ashr

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`m`	What is this argument?
`thresh`	the threshold below which components are removed