<?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>Ordinal Causal Discovery</dc:title>
  <dc:title>R package OrdCD version 1.1.2</dc:title>
  <dc:description>Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., &amp; Mallick, B. (2022) &lt;https://proceedings.mlr.press/v180/ni22a/ni22a.pdf&gt; "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: gRbase, MASS, bnlearn, igraph, stats, Matrix</dc:relation>
  <dc:creator>Yang Ni &lt;yni@stat.tamu.edu&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Yang Ni [aut, cre] (ORCID: &lt;https://orcid.org/0000-0003-0636-2363&gt;)</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=OrdCD/LICENSE)</dc:rights>
  <dc:date>2023-05-17</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=OrdCD</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.OrdCD</dc:identifier>
</oai_dc:dc>