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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 |
| Author: | Evan A Martin [aut, cre], Audrey Qiuyan Fu [aut] |
| Maintainer: | Evan A Martin <evanamartin at gmail.com> |
| 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: baycn_1.2.0.zip, r-release: baycn_1.2.0.zip, r-oldrel: baycn_1.2.0.zip |
| 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 |
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