Package: sparsebn
Title: Learning Sparse Bayesian Networks from High-Dimensional Data
Version: 0.1.0
Date: 2019-11-05
Authors@R: c(
    person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre")),
    person("Jiaying", "Gu", role = c("aut")),
    person("Dacheng", "Zhang", role = c("aut")),
    person("Qing", "Zhou", role = c("aut"))
    )
Maintainer: Bryon Aragam <sparsebn@gmail.com>
Description: Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
Depends: R (>= 3.2.3), sparsebnUtils (>= 0.0.5), ccdrAlgorithm (>=
        0.0.4), discretecdAlgorithm (>= 0.0.5)
Suggests: knitr, rmarkdown, mvtnorm, igraph, graph, testthat
URL: https://github.com/itsrainingdata/sparsebn
BugReports: https://github.com/itsrainingdata/sparsebn/issues
License: GPL (>= 2)
RoxygenNote: 6.1.1
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2019-11-04 18:25:46 UTC; naragam
Author: Bryon Aragam [aut, cre],
  Jiaying Gu [aut],
  Dacheng Zhang [aut],
  Qing Zhou [aut]
Repository: CRAN
Date/Publication: 2019-11-04 19:50:02 UTC
Built: R 3.5.2; ; 2019-11-08 18:03:13 UTC; unix
