COLP: Causal Discovery for Categorical Data with Label Permutation
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  [aut,
cre] |
| Maintainer: | Yang Ni <yni at stat.tamu.edu> |
| BugReports: | https://github.com/nySTAT/COLP/issues |
| License: | MIT + file LICENSE |
| 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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