<?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>Compute the Causal Win Ratio Using Nearest Neighbor Matching</dc:title>
  <dc:title>R package causalWins version 0.1.0</dc:title>
  <dc:description>Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: drf, FactoMineR, grf, MatchIt</dc:relation>
  <dc:relation>Suggests: knitr, rmarkdown, WINS, MASS</dc:relation>
  <dc:creator>Mathieu Even &lt;mathieu.even@inria.fr&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Francisco Andrade [aut],
  Mathieu Even [aut, cre],
  Julie Josse [aut]</dc:contributor>
  <dc:rights>AGPL (&gt;= 3)</dc:rights>
  <dc:date>2026-04-21</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=causalWins</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.causalWins</dc:identifier>
</oai_dc:dc>