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: | 0.4.5 |
| 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: | 2020-06-11 |
| 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_0.4.5.tar.gz |
| Windows binaries: | r-devel: dbnR_0.4.5.zip, r-release: dbnR_0.4.5.zip, r-oldrel: dbnR_0.4.5.zip |
| macOS binaries: | r-release: dbnR_0.4.5.tgz, r-oldrel: dbnR_0.4.5.tgz |
| Old sources: | dbnR archive |
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