CRAN

rjmcmc 0.4.4

Reversible-Jump MCMC Using Post-Processing

Released Mar 2, 2019 by Nick Gelling

This package can be loaded by Renjin but 2 out 4 tests failed.

Dependencies

madness 0.2.3 mvtnorm 1.0-9 coda 0.19-2

Performs reversible-jump Markov chain Monte Carlo (Green, 1995) , specifically the restriction introduced by Barker & Link (2013) . By utilising a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling problem. Previously-calculated posterior distributions are used to quickly estimate posterior model probabilities. Jacobian matrices are found using automatic differentiation.

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>rjmcmc</artifactId>
    <version>0.4.4-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:rjmcmc')

Test Results

This package was last tested against Renjin 0.9.2724 on Mar 4, 2019.

Source

R

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Release History