Encoding: UTF-8
Package: baycn
Type: Package
Title: Bayesian Inference for Causal Networks
Version: 2.0.0
Authors@R: c(person(given = c("Evan", "A"),
                      family = "Martin",
                      role = c("aut")),
               person(given = "Venkata",
                      family = "Patchigolla",
                      role = "ctb"),
               person(given = "Audrey",
                      family = "Fu",
                      role = c("aut", "cre"),
                      email = "audreyqyfu@gmail.com"))
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 Metropolis-Hastings-like 
    sampling algorithm is then used to infer the posterior probabilities of 
    edge direction and edge absence.
    References:
    Martin, Patchigolla and Fu (2026) <doi:10.48550/arXiv.1909.10678>.
License: GPL-3 | file LICENSE
LazyData: true
Depends: R (>= 3.5.0)
Imports: egg, ggplot2, igraph, MASS, methods, expm
RoxygenNote: 7.3.3
Suggests: testthat
NeedsCompilation: no
Packaged: 2026-03-09 01:59:36 UTC; audreyq.fu
Author: Evan A Martin [aut],
  Venkata Patchigolla [ctb],
  Audrey Fu [aut, cre]
Maintainer: Audrey Fu <audreyqyfu@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-10 09:40:40 UTC
Built: R 4.6.0; ; 2026-04-25 16:12:47 UTC; unix
