Causal Discovery for Categorical Data with Label Permutation
Overview
Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) arXiv:2209.08579 "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Yang Ni <yni@stat.tamu.edu>’ The Description field contains categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 2
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
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1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/nystat/colp on 2026-10-21.
Dependencies
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.0.0Latest2022-09-29 · current release
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-09-29
- Total releases
- 1 / 4 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 5.5 KB / 1 file
- Download size
- 8.6 KB
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