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.

Documentation

Manual: ccdrAlgorithm.pdf
Vignette: None available.

Maintainer: Bryon Aragam <sparsebn at gmail.com>

Author(s): Bryon Aragam*, Dacheng Zhang*

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

install.packages("ccdrAlgorithm")

Depends R (>= 3.2.3)
Imports sparsebnUtils(>=0.0.5), Rcpp(>=0.11.0), stats, utils
Suggests testthat, graph, igraph
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Package ccdrAlgorithm
Materials
URL https://github.com/itsrainingdata/ccdrAlgorithm
Task Views
Version 0.0.4
Published 2017-09-11
License GPL (>= 2)
BugReports https://github.com/itsrainingdata/ccdrAlgorithm/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks ccdrAlgorithm check results
Package source ccdrAlgorithm_0.0.4.tar.gz