sparsebn: Learning Sparse Bayesian Networks from High-Dimensional Data

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.

Version: 0.1.2
Depends: R (≥ 3.2.3), sparsebnUtils (≥ 0.0.5), ccdrAlgorithm (≥ 0.0.4), discretecdAlgorithm (≥ 0.0.5)
Suggests: knitr, rmarkdown, mvtnorm, igraph, graph, testthat
Published: 2020-09-13
Author: Bryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut]
Maintainer: Bryon Aragam <sparsebn at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: sparsebn citation info
Materials: README NEWS
In views: gR
CRAN checks: sparsebn results


Reference manual: sparsebn.pdf
Vignettes: Introduction to sparsebn
Package source: sparsebn_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: sparsebn_0.1.2.tgz, r-oldrel: sparsebn_0.1.2.tgz
Old sources: sparsebn archive

Reverse dependencies:

Reverse imports: glmSparseNet


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