The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

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
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.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_1.1.1.zip, r-release: BCDAG_1.1.1.zip, r-oldrel: BCDAG_1.1.1.zip
macOS binaries: r-release (arm64): BCDAG_1.1.1.tgz, r-oldrel (arm64): BCDAG_1.1.1.tgz, r-release (x86_64): BCDAG_1.1.1.tgz, r-oldrel (x86_64): BCDAG_1.1.1.tgz
Old sources: BCDAG archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BCDAG to link to this page.

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.