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 three 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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.7.1 by David Quesada, 4 months ago


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, R6, methods

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

Linking to Rcpp

See at CRAN