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: | 0.0.4 |
| Depends: | R (≥ 3.2.3) |
| Imports: | sparsebnUtils (≥ 0.0.5), Rcpp (≥ 0.11.0), stats, utils |
| LinkingTo: | Rcpp |
| Suggests: | testthat, graph, igraph |
| Published: | 2017-09-11 |
| 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 |
| Reference manual: | ccdrAlgorithm.pdf |
| Package source: | ccdrAlgorithm_0.0.4.tar.gz |
| Windows binaries: | r-devel: ccdrAlgorithm_0.0.4.zip, r-release: ccdrAlgorithm_0.0.4.zip, r-oldrel: ccdrAlgorithm_0.0.4.zip |
| OS X El Capitan binaries: | r-release: ccdrAlgorithm_0.0.4.tgz |
| OS X Mavericks binaries: | r-oldrel: ccdrAlgorithm_0.0.4.tgz |
| Old sources: | ccdrAlgorithm archive |
| Reverse depends: | sparsebn |
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