CRAN

ashr 2.0.5

Methods for Adaptive Shrinkage, using Empirical Bayes

Released Dec 21, 2016 by Matthew Stephens, Chaoxing Dai, Mengyin Lu, David Gerard, Nan Xiao, Peter Carbonetto

This package can be loaded by Renjin but 11 out 16 tests failed.

Dependencies

foreach 1.4.3 truncnorm 1.0-7 doParallel 1.0.11 assertthat 0.2.0 etrunct 0.1 pscl 1.5.2 SQUAREM 2017.10-1 Rcpp

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.

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>2.0.5-b11</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 0.8.2523 on Nov 12, 2017.

Source

R
C++

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Release History