cia: Learn and Apply Directed Acyclic Graphs for Causal Inference

Causal Inference Assistance (CIA) for performing causal inference within the structural causal modelling framework. Structure learning is performed using partition Markov chain Monte Carlo (Kuipers & Moffa, 2017) and several additional functions have been added to help with causal inference. Kuipers and Moffa (2017) <doi:10.1080/01621459.2015.1133426>.

Version:	1.0.0
Depends:	R (≥ 4.4.0)
Imports:	bnlearn (≥ 4.9), igraph, doParallel, parallel, foreach, arrangements, graphics, dplyr, rlang, fastmatch, methods, gRain, patchwork, tidyr
Suggests:	rmarkdown, knitr, testthat (≥ 3.0.0), gtools, gRbase, ggplot2, qgraph, dagitty
Published:	2024-11-13
DOI:	10.32614/CRAN.package.cia
Author:	Mathew Varidel [aut, cre, cph], Victor An [ctb]
Maintainer:	Mathew Varidel <mathew.varidel at sydney.edu.au>
BugReports:	https://github.com/SpaceOdyssey/cia/issues
License:	MIT + file LICENSE
URL:	https://spaceodyssey.github.io/cia/
NeedsCompilation:	no
Citation:	cia citation info
Materials:	README NEWS
CRAN checks:	cia results

Documentation:

Reference manual:

cia.pdf

Downloads:

Package source:	cia_1.0.0.tar.gz
Windows binaries:	r-devel: cia_1.0.0.zip, r-release: cia_1.0.0.zip, r-oldrel: not available
macOS binaries:	r-release (arm64): cia_1.0.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): cia_1.0.0.tgz, r-oldrel (x86_64): not available

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