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) <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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=baycn
to link to this page.