<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Implementation of a Generic Adaptive Monte Carlo Markov Chain
Sampler</dc:title>
  <dc:title>R package adaptMCMC version 1.5</dc:title>
  <dc:description>Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) &lt;DOI:10.1007/s11222-011-9269-5&gt; and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.14.1), parallel, coda, Matrix</dc:relation>
  <dc:relation>Imports: ramcmc</dc:relation>
  <dc:creator>Andreas Scheidegger &lt;andreas.scheidegger@eawag.ch&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Andreas Scheidegger, &lt;andreas.scheidegger@eawag.ch&gt;, &lt;scheidegger.a@gmail.com&gt;</dc:contributor>
  <dc:rights>GPL (&gt;= 2)</dc:rights>
  <dc:date>2024-01-29</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=adaptMCMC</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.adaptMCMC</dc:identifier>
</oai_dc:dc>