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 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: |
0.7.5 |
| 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), methods (≥ 3.6.0) |
| 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: |
2022-03-14 |
| 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:
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