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.
| Version: | 0.1.0 |
| Imports: | drf, FactoMineR, grf, MatchIt |
| Suggests: | knitr, rmarkdown, WINS, MASS |
| Published: | 2026-04-21 |
| DOI: | 10.32614/CRAN.package.causalWins (may not be active yet) |
| Author: | Francisco Andrade [aut], Mathieu Even [aut, cre], Julie Josse [aut] |
| Maintainer: | Mathieu Even <mathieu.even at inria.fr> |
| License: | AGPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | causalWins results |
| Reference manual: | causalWins.html , causalWins.pdf |
| Vignettes: |
A Causal Framework for Hierarchical Outcome Analysis (source, R code) |
| Package source: | causalWins_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: causalWins_0.1.0.zip, r-oldrel: causalWins_0.1.0.zip |
| macOS binaries: | r-release (arm64): causalWins_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): causalWins_0.1.0.tgz, r-oldrel (x86_64): causalWins_0.1.0.tgz |
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