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 three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers 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: 0.7.9 Depends: R (≥ 3.5.0), bnlearn (≥ 4.5) Imports: data.table (≥ 1.12.4), Rcpp (≥ 1.0.2), magrittr (≥ 1.5), R6 (≥ 2.4.1), stats (≥ 3.6.0), MASS (≥ 7.3-55) LinkingTo: Rcpp Suggests: visNetwork (≥ 2.0.8), grDevices (≥ 3.6.0), utils (≥ 3.6.0), graphics (≥ 3.6.0), testthat (≥ 2.1.0) Published: 2024-06-19 DOI: 10.32614/CRAN.package.dbnR 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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=dbnR to link to this page.
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