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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:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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