CRAN

ashr 2.2-7

Methods for Adaptive Shrinkage, using Empirical Bayes

Released Mar 1, 2018 by Peter Carbonetto

This package can be loaded by Renjin but 14 out 19 tests failed.

Dependencies

pscl 1.5.2 Rcpp etrunct 0.1 assertthat 0.2.0 doParallel 1.0.11 foreach 1.4.4 SQUAREM 2017.10-1 Matrix 1.2-12 truncnorm 1.0-8

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).

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>ashr</artifactId>
    <version>2.2-7-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

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Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:ashr')

Test Results

This package was last tested against Renjin 0.9.2597 on Mar 3, 2018.

Source

R
C++

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Release History