BiCausality: Binary Causality Inference Framework

A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

Version: 0.1.4
Depends: R (≥ 3.5.0)
Suggests: knitr, rmarkdown, markdown, igraph
Published: 2023-11-28
DOI: 10.32614/CRAN.package.BiCausality
Author: Chainarong Amornbunchornvej ORCID iD [aut, cre]
Maintainer: Chainarong Amornbunchornvej <grandca at>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: BiCausality citation info
Materials: README NEWS
CRAN checks: BiCausality results


Reference manual: BiCausality.pdf
Vignettes: BiCausality_demo


Package source: BiCausality_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BiCausality_0.1.4.tgz, r-oldrel (arm64): BiCausality_0.1.4.tgz, r-release (x86_64): BiCausality_0.1.4.tgz, r-oldrel (x86_64): BiCausality_0.1.4.tgz
Old sources: BiCausality archive


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