OrdCD: Ordinal Causal Discovery

Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".

Version: 1.0.0
Imports: gRbase, MASS, bnlearn, igraph, stats
Published: 2022-09-27
Author: Yang Ni ORCID iD [aut, cre]
Maintainer: Yang Ni <yni at stat.tamu.edu>
BugReports: https://github.com/nySTAT/OCD/issues
License: MIT + file LICENSE
URL: https://github.com/nySTAT/OCD
NeedsCompilation: no
CRAN checks: OrdCD results

Documentation:

Reference manual: OrdCD.pdf

Downloads:

Package source: OrdCD_1.0.0.tar.gz
Windows binaries: r-devel: OrdCD_1.0.0.zip, r-release: OrdCD_1.0.0.zip, r-oldrel: OrdCD_1.0.0.zip
macOS binaries: r-release (arm64): OrdCD_1.0.0.tgz, r-oldrel (arm64): OrdCD_1.0.0.tgz, r-release (x86_64): OrdCD_1.0.0.tgz, r-oldrel (x86_64): OrdCD_1.0.0.tgz

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