r.blip: Bayesian Network Learning Improved Project

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the 'BIC' and the 'BDeu' score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

Version: 1.1
Depends: R (≥ 3.0.0)
Imports: foreign, bnlearn (≥ 4.0)
Published: 2019-02-27
Author: Mauro Scanagatta [aut, cre]
Maintainer: Mauro Scanagatta <mauro at idsia.ch>
License: LGPL-3
NeedsCompilation: no
SystemRequirements: Java (>= 1.5)
CRAN checks: r.blip results


Reference manual: r.blip.pdf
Package source: r.blip_1.1.tar.gz
Windows binaries: r-devel: r.blip_1.1.zip, r-release: r.blip_1.1.zip, r-oldrel: r.blip_1.1.zip
macOS binaries: r-release: r.blip_1.1.tgz, r-oldrel: r.blip_1.1.tgz


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