Evatar/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>.

Getting started

Package details

AuthorEvan A Martin [aut, cre], Audrey Qiuyan Fu [aut]
MaintainerEvan A Martin <[email protected]>
LicenseGPL-3 | file LICENSE
Version1.0.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("Evatar/baycn")
Evatar/baycn documentation built on Feb. 1, 2020, 2:07 a.m.