bnclassify: Learning Discrete Bayesian Network Classifiers from Data

Implements state-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, as well as functions for using these classifiers for prediction, assessing their predictive performance, and inspecting their properties.

Version: 0.3.4
Depends: R (≥ 3.2.0)
Imports: assertthat (≥ 0.1), entropy (≥ 1.2.0), graph (≥ 1.42.0), matrixStats (≥ 0.14.0), RBGL (≥ 1.40.1), rpart (≥ 4.1-8)
Suggests: gRain (≥ 1.2-3), gRbase (≥ 1.7-0.1), mlr (≥ 2.2), testthat (≥ 0.8.1), knitr (≥ 1.10.5), ParamHelpers (≥ 1.5), Rgraphviz (≥ 2.8.1), rmarkdown (≥ 0.7), covr
Published: 2018-01-13
Author: Mihaljevic Bojan [aut, cre], Bielza Concha [aut], Larranaga Pedro [aut], Wickham Hadley [ctb] (some code extracted from memoise package)
Maintainer: Mihaljevic Bojan <bmihaljevic at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bnclassify results


Reference manual: bnclassify.pdf
Vignettes: Introduction
Techical information
Package source: bnclassify_0.3.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: bnclassify_0.3.4.tgz, r-oldrel: bnclassify_0.3.4.tgz
Old sources: bnclassify archive


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