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) 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.


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install.packages("dbnR")

0.4.5 by David Quesada, 4 months ago


https://github.com/dkesada/dbnR


Browse source code at https://github.com/cran/dbnR


Authors: David Quesada [aut, cre] , Gabriel Valverde [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports bnlearn, data.table, Rcpp, magrittr

Suggests visNetwork, grDevices, utils, graphics, stats, testthat

Linking to Rcpp


See at CRAN