sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data

Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization. Designed to incorporate mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Version: 0.0.2
Depends: R (≥ 3.2.3), sparsebnUtils (≥ 0.0.3), ccdrAlgorithm (≥ 0.0.2), discretecdAlgorithm (≥ 0.0.2)
Suggests: knitr, rmarkdown, mvtnorm, igraph, graph, testthat
Published: 2016-11-29
Author: Bryon Aragam [aut, cre]
Maintainer: Bryon Aragam <sparsebn at gmail.com>
BugReports: https://github.com/itsrainingdata/sparsebn/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/itsrainingdata/sparsebn
NeedsCompilation: no
Citation: sparsebn citation info
Materials: README NEWS
CRAN checks: sparsebn results

Downloads:

Reference manual: sparsebn.pdf
Vignettes: Introduction to sparsebn
Package source: sparsebn_0.0.2.tar.gz
Windows binaries: r-devel: sparsebn_0.0.2.zip, r-release: sparsebn_0.0.2.zip, r-oldrel: sparsebn_0.0.2.zip
OS X Mavericks binaries: r-release: sparsebn_0.0.2.tgz, r-oldrel: sparsebn_0.0.2.tgz
Old sources: sparsebn archive

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