Encoding: UTF-8
Package: baycn
Type: Package
Title: Bayesian Inference for Causal Networks
Version: 1.3.0
Authors@R: c(person(given = c("Evan", "A"),
                      family = "Martin",
                      role = c("aut", "cre"),
                      email = "evanamartin@gmail.com"),
               person(given = "Venkata",
                      family = "Patchigolla",
                      role = "ctb"),
               person(given = "Audrey",
                      family = "Fu",
                      role = "aut"))
Description: 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>.
License: GPL-3 | file LICENSE
LazyData: true
Depends: R (>= 3.5.0)
Imports: egg, ggplot2, gtools, igraph, MASS, methods, parallel,
        foreach, doParallel
RoxygenNote: 7.3.3
Suggests: testthat
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://evanamartin.r-universe.dev
Date/Publication: 2025-11-07 16:53:51 UTC
RemoteUrl: https://github.com/evanamartin/baycn
RemoteRef: HEAD
RemoteSha: 98efa1928830b8ba2a24941be1975d4c793c2245
NeedsCompilation: no
Packaged: 2026-06-20 06:39:28 UTC; root
Author: Evan A Martin [aut, cre],
  Venkata Patchigolla [ctb],
  Audrey Fu [aut]
Maintainer: Evan A Martin <evanamartin@gmail.com>