baycn: Bayesian Inference for Causal Networks

A Bayesian hybrid approach for inferring Directed Acyclic Graphs (DAGs) for continuous, discrete, and mixed data. The algorithm can use the graph inferred by another more efficient graph inference method as input; the input graph may contain false edges or undirected edges but can help reduce the search space to a more manageable size. A Bayesian Markov chain Monte Carlo algorithm is then used to infer the probability of direction and absence for the edges in the network. References: Martin and Fu (2019) <doi:10.48550/arXiv.1909.10678>.

Version: 1.2.0
Depends: R (≥ 3.5.0)
Imports: egg, ggplot2, gtools, igraph, MASS, methods
Suggests: testthat
Published: 2020-07-31
DOI: 10.32614/CRAN.package.baycn
Author: Evan A Martin [aut, cre], Audrey Qiuyan Fu [aut]
Maintainer: Evan A Martin <evanamartin at>
License: GPL-3 | file LICENSE
NeedsCompilation: no
In views: Bayesian
CRAN checks: baycn results


Reference manual: baycn.pdf


Package source: baycn_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): baycn_1.2.0.tgz, r-oldrel (arm64): baycn_1.2.0.tgz, r-release (x86_64): baycn_1.2.0.tgz, r-oldrel (x86_64): baycn_1.2.0.tgz
Old sources: baycn archive


Please use the canonical form to link to this page.