Package: COLP 1.0.0

Yang Ni

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

Authors:Yang Ni [aut, cre]

COLP_1.0.0.tar.gz
COLP_1.0.0.zip(r-4.7-any)COLP_1.0.0.zip(r-4.6-any)COLP_1.0.0.zip(r-4.5-any)
COLP_1.0.0.tgz(r-4.6-any)COLP_1.0.0.tgz(r-4.5-any)
COLP_1.0.0.tar.gz(r-4.7-any)COLP_1.0.0.tar.gz(r-4.6-any)
COLP_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
COLP/json (API)

# Install 'COLP' in R:

install.packages('COLP', repos = c('https://nystat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nystat/colp/issues

Datasets:

CatPairs - Categorical Cause-Effect Pairs

On CRAN:

2.70 score 1 stars 1 scripts 317 downloads 1 exports 2 dependencies

Last updated from:3bd5f55bef. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	89
source / vignettes	OK	158
linux-release	OK	85
macos-release	OK	99
macos-oldrel	OK	88
windows-devel	OK	60
windows-release	OK	61
windows-oldrel	OK	56
wasm-release	OK	84