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: 1.1.1
Depends: R (≥ 2.10)
Imports: graph, graphics, gRbase, grDevices, lattice, methods, mvtnorm, Rgraphviz, stats, utils
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0)
Published: 2024-06-14
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.pdf
Vignettes: Random data generation from Gaussian DAG models
Elaborate on the output of 'learn_DAG()' using get_ functions
MCMC scheme for posterior inference of Gaussian DAG models: the 'learn_DAG()' function

Downloads:

Package source: BCDAG_1.1.1.tar.gz
Windows binaries: r-devel: BCDAG_1.1.0.zip, r-release: BCDAG_1.1.0.zip, r-oldrel: BCDAG_1.1.0.zip
macOS binaries: r-release (arm64): BCDAG_1.1.0.tgz, r-oldrel (arm64): BCDAG_1.1.0.tgz, r-release (x86_64): BCDAG_1.1.0.tgz, r-oldrel (x86_64): BCDAG_1.1.0.tgz
Old sources: BCDAG archive

Linking:

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