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
Author:	Chainarong Amornbunchornvej [aut, cre]
Maintainer:	Chainarong Amornbunchornvej <grandca at gmail.com>
BugReports:	https://github.com/DarkEyes/BiCausality/issues
License:	MIT + file LICENSE
URL:	https://github.com/DarkEyes/BiCausality
NeedsCompilation:	no
Citation:	BiCausality citation info
Materials:	README NEWS
CRAN checks:	BiCausality results

Documentation:

Reference manual:	BiCausality.pdf
Vignettes:	BiCausality_demo

Downloads:

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

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