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) <arXiv:2201.12003>.

Version:	1.0.0
Depends:	R (≥ 2.10)
Imports:	graphics, gRbase, grDevices, lattice, methods, mvtnorm, stats, utils
Suggests:	rmarkdown, knitr, testthat (≥ 3.0.0)
Published:	2022-03-15
Author:	Federico Castelletti [aut], Alessandro Mascaro [aut, cre]
Maintainer:	Alessandro Mascaro <a.mascaro3 at campus.unimib.it>
License:	MIT + file LICENSE
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_1.0.0.tar.gz
Windows binaries:	r-devel: BCDAG_1.0.0.zip, r-release: BCDAG_1.0.0.zip, r-oldrel: BCDAG_1.0.0.zip
macOS binaries:	r-release (arm64): BCDAG_1.0.0.tgz, r-oldrel (arm64): BCDAG_1.0.0.tgz, r-release (x86_64): BCDAG_1.0.0.tgz, r-oldrel (x86_64): BCDAG_1.0.0.tgz

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