ccdrAlgorithm: CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks

Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.

Version: 0.0.5
Depends: R (≥ 3.2.3)
Imports: sparsebnUtils (≥ 0.0.5), Rcpp (≥ 0.11.0), stats, utils
LinkingTo: Rcpp
Suggests: testthat, graph, igraph, Matrix
Published: 2018-06-01
Author: Bryon Aragam [aut, cre], Dacheng Zhang [aut]
Maintainer: Bryon Aragam <sparsebn at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: ccdrAlgorithm citation info
Materials: README NEWS
CRAN checks: ccdrAlgorithm results


Reference manual: ccdrAlgorithm.pdf


Package source: ccdrAlgorithm_0.0.5.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ccdrAlgorithm_0.0.5.tgz, r-release (x86_64): ccdrAlgorithm_0.0.5.tgz, r-oldrel: ccdrAlgorithm_0.0.5.tgz
Old sources: ccdrAlgorithm archive

Reverse dependencies:

Reverse depends: sparsebn


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