<?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>Estimate Causal Polytree from Data</dc:title>
  <dc:title>R package PolyTree version 0.0.1</dc:title>
  <dc:description>Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) &lt;arxiv:2209.07028&gt;. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: FOCI, igraph</dc:relation>
  <dc:creator>Sourav Chatterjee &lt;souravc@stanford.edu&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Sourav Chatterjee [aut, cre] (ORCID:
    &lt;https://orcid.org/0000-0003-4460-209X&gt;)</dc:contributor>
  <dc:rights>MIT + file LICENSE (https://CRAN.R-project.org/package=PolyTree/LICENSE)</dc:rights>
  <dc:date>2024-03-25</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=PolyTree</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.PolyTree</dc:identifier>
</oai_dc:dc>