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 two structure learning algorithms for dynamic Bayesian networks 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: 0.5.3
Depends: R (≥ 3.5.0)
Imports: bnlearn (≥ 4.5), data.table (≥ 1.12.4), Rcpp (≥ 1.0.2), magrittr (≥ 1.5), R6 (≥ 2.4.1)
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: 2020-10-13
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


Reference manual: dbnR.pdf
Package source: dbnR_0.5.3.tar.gz
Windows binaries: r-devel: dbnR_0.5.3.zip, r-release: dbnR_0.5.3.zip, r-oldrel: dbnR_0.5.3.zip
macOS binaries: r-release: dbnR_0.5.3.tgz, r-oldrel: dbnR_0.5.3.tgz
Old sources: dbnR archive


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