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 two 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.

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.

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.

Dynamic Bayesian Network Structure Learning, Parameter Learning
and Forecasting
It allows to learn the structure of univariate time series, learning parameters and forecasting.
Implements a model of Dynamic Bayesian Networks with temporal windows,
with collections of linear regressors for Gaussian nodes,
based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and
Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.