Package: baycn
Type: Package
Title: Bayesian Inference for Causal Networks
Version: 1.2.0
Author: Evan A Martin [aut, cre], Audrey Qiuyan Fu [aut]
Maintainer: Evan A Martin <evanamartin@gmail.com>
Description: 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>.
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.5.0)
Imports: egg, ggplot2, gtools, igraph, MASS, methods
RoxygenNote: 7.1.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2020-07-26 01:03:48 UTC; Evatar
Repository: CRAN
Date/Publication: 2020-07-31 18:40:06 UTC
Built: R 4.1.0; ; 2020-08-02 23:44:16 UTC; windows
