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An approximate Bayesian method 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 Metropolis-Hastings-like sampling algorithm is then used to infer the posterior probabilities of edge direction and edge absence. References: Martin, Patchigolla and Fu (2026) <doi:10.48550/arXiv.1909.10678>.
| Version: | 2.0.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | egg, ggplot2, igraph, MASS, methods, expm |
| Suggests: | testthat |
| Published: | 2026-03-10 |
| DOI: | 10.32614/CRAN.package.baycn |
| Author: | Evan A Martin [aut], Venkata Patchigolla [ctb], Audrey Fu [aut, cre] |
| Maintainer: | Audrey Fu <audreyqyfu at gmail.com> |
| License: | GPL-3 | file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | baycn results |
| Reference manual: | baycn.html , baycn.pdf |
| Package source: | baycn_2.0.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): baycn_2.0.0.tgz, r-oldrel (arm64): baycn_2.0.0.tgz, r-release (x86_64): baycn_2.0.0.tgz, r-oldrel (x86_64): baycn_2.0.0.tgz |
| Old sources: | baycn archive |
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