doi:10.48550/arXiv.2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".">

COLP: Causal Discovery for Categorical Data with Label Permutation (original) (raw)

Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <doi:10.48550/arXiv.2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: MASS, combinat, stats
Published: 2022-09-29
DOI: 10.32614/CRAN.package.COLP
Author: Yang Ni ORCID iD [aut, cre]
Maintainer: Yang Ni
BugReports: https://github.com/nySTAT/COLP/issues
License: MIT + file
URL: https://github.com/nySTAT/COLP
NeedsCompilation: no
CRAN checks: COLP results

Documentation:

Reference manual: COLP.html , <COLP.pdf>

Downloads:

Package source: COLP_1.0.0.tar.gz
Windows binaries: r-devel: COLP_1.0.0.zip, r-release: COLP_1.0.0.zip, r-oldrel: COLP_1.0.0.zip
macOS binaries: r-release (arm64): COLP_1.0.0.tgz, r-oldrel (arm64): COLP_1.0.0.tgz, r-release (x86_64): COLP_1.0.0.tgz, r-oldrel (x86_64): COLP_1.0.0.tgz

Linking:

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