Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in as described in Aragam, Gu, and Zhou (2017) . Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Documentation

Manual: sparsebn.pdf
Vignette: Introduction to sparsebn

Maintainer: Bryon Aragam <sparsebn at gmail.com>

Author(s): Bryon Aragam*

Install package and any missing dependencies by running this line in your R console:

install.packages("sparsebn")

Depends R (>= 3.2.3), sparsebnUtils(>=0.0.4), ccdrAlgorithm(>=0.0.3), discretecdAlgorithm(>=0.0.3)
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Suggests knitr, rmarkdown, mvtnorm, igraph, graph, testthat
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Package sparsebn
Materials
URL https://github.com/itsrainingdata/sparsebn
Task Views
Version 0.0.4
Published 2017-03-16
License GPL (>= 2)
BugReports https://github.com/itsrainingdata/sparsebn/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks sparsebn check results
Package source sparsebn_0.0.4.tar.gz