Package: baycn 1.3.0

baycn: Bayesian Inference for Causal Networks

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 Markov chain Monte Carlo-like algorithm is then used to infer the posterior probabilities of edge direction and edge absence. References: Martin and Fu (2019) <doi:10.48550/arXiv.1909.10678>.

Authors:Evan A Martin [aut, cre], Venkata Patchigolla [ctb], Audrey Fu [aut]