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) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.

Version: 0.0.2
Depends: R (≥ 3.2.3)
Imports: sparsebnUtils (≥ 0.0.2), Rcpp (≥ 0.11.0)
LinkingTo: Rcpp
Suggests: testthat, graph
Published: 2016-11-20
Author: Bryon Aragam [aut, cre], Dacheng Zhang [aut]
Maintainer: Bryon Aragam <sparsebn at gmail.com>
BugReports: https://github.com/itsrainingdata/ccdrAlgorithm/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/itsrainingdata/ccdrAlgorithm
NeedsCompilation: yes
Citation: ccdrAlgorithm citation info
Materials: README NEWS
CRAN checks: ccdrAlgorithm results

Downloads:

Reference manual: ccdrAlgorithm.pdf
Package source: ccdrAlgorithm_0.0.2.tar.gz
Windows binaries: r-devel: ccdrAlgorithm_0.0.2.zip, r-release: ccdrAlgorithm_0.0.2.zip, r-oldrel: ccdrAlgorithm_0.0.2.zip
OS X Mavericks binaries: r-release: ccdrAlgorithm_0.0.2.tgz, r-oldrel: ccdrAlgorithm_0.0.2.tgz
Old sources: ccdrAlgorithm archive

Reverse dependencies:

Reverse depends: sparsebn

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