BCDAG: Bayesian Structure and Causal Learning of Gaussian Directed Graphs (original) (raw)
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.
| Version: | 1.1.3 |
|---|---|
| Depends: | R (≥ 2.10) |
| Imports: | graph, graphics, gRbase, Rgraphviz, grDevices, lattice, methods, mvtnorm, stats, utils |
| Suggests: | rmarkdown, knitr, testthat (≥ 3.0.0) |
| Published: | 2025-02-28 |
| DOI: | 10.32614/CRAN.package.BCDAG |
| Author: | Federico Castelletti [aut], Alessandro Mascaro [aut, cre, cph] |
| Maintainer: | Alessandro Mascaro <alessandro.mascaro at upf.edu> |
| BugReports: | https://github.com/alesmascaro/BCDAG/issues |
| License: | MIT + file |
| URL: | https://github.com/alesmascaro/BCDAG |
| NeedsCompilation: | no |
| Materials: | README, NEWS |
| CRAN checks: | BCDAG results |
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