Bayesian Structure and Causal Learning of Gaussian Directed Graphs [R package BCDAG version 1.1.3]

BCDAG: Bayesian Structure and Causal Learning of Gaussian Directed Graphs

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年02月28日

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 LICENSE

URL: https://github.com/alesmascaro/BCDAG

NeedsCompilation: no

Materials: README, NEWS

CRAN checks: BCDAG results

Documentation:

Reference manual: BCDAG.html , BCDAG.pdf

Vignettes: Random data generation from Gaussian DAG models (source, R code)
Elaborate on the output of 'learn_DAG()' using get_ functions (source, R code)
MCMC scheme for posterior inference of Gaussian DAG models: the 'learn_DAG()' function (source, R code)

Downloads:

Package source: BCDAG_1.1.3.tar.gz

Windows binaries: r-devel: BCDAG_1.1.3.zip, r-release: BCDAG_1.1.3.zip, r-oldrel: BCDAG_1.1.3.zip

macOS binaries: r-release (arm64): BCDAG_1.1.3.tgz, r-oldrel (arm64): BCDAG_1.1.3.tgz, r-release (x86_64): BCDAG_1.1.3.tgz, r-oldrel (x86_64): BCDAG_1.1.3.tgz

Old sources: BCDAG archive

Linking:

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