dbnR: Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers a modification of Trabelsi (2013) <doi:10.1007/978-3-642-41398-8_34> dynamic max-min hill climbing algorithm for structure learning and the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Version: 0.3.4
Depends: R (≥ 3.5.0)
Imports: bnlearn (≥ 4.5), data.table (≥ 1.12.4), Rcpp (≥ 1.0.2), magrittr (≥ 1.5)
LinkingTo: Rcpp
Suggests: visNetwork (≥ 2.0.8), grDevices (≥ 3.6.0), utils (≥ 3.6.0), graphics (≥ 3.6.0), stats (≥ 3.6.0), testthat (≥ 2.1.0)
Published: 2020-03-25
Author: David Quesada [aut, cre], Gabriel Valverde [ctb]
Maintainer: David Quesada <dkesada at gmail.com>
License: GPL-3
URL: https://github.com/dkesada/dbnR
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: dbnR results

Downloads:

Reference manual: dbnR.pdf
Package source: dbnR_0.3.4.tar.gz
Windows binaries: r-devel: dbnR_0.3.4.zip, r-devel-gcc8: dbnR_0.3.4.zip, r-release: dbnR_0.3.4.zip, r-oldrel: dbnR_0.3.4.zip
OS X binaries: r-release: dbnR_0.3.4.tgz, r-oldrel: not available
Old sources: dbnR archive

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