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 <arXiv:2205.06131>.
Version: | 0.1.2 |
Depends: | R (≥ 3.5.0) |
Suggests: | knitr, rmarkdown, markdown, igraph |
Published: | 2022-08-19 |
Author: | Chainarong Amornbunchornvej
|
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 |
Reference manual: | BiCausality.pdf |
Vignettes: |
BiCausality_demo |
Package source: | BiCausality_0.1.2.tar.gz |
Windows binaries: | r-devel: BiCausality_0.1.2.zip, r-release: BiCausality_0.1.2.zip, r-oldrel: BiCausality_0.1.2.zip |
macOS binaries: | r-release (arm64): BiCausality_0.1.2.tgz, r-oldrel (arm64): BiCausality_0.1.2.tgz, r-release (x86_64): BiCausality_0.1.2.tgz, r-oldrel (x86_64): BiCausality_0.1.2.tgz |
Old sources: | BiCausality archive |
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