dmmhc

the data.frame or data.table to be used

number of time slices of the net. Markovian 1 would be size 2

size

previously folded dataset, in case some specific rows have to be removed after the folding

f_dt

an optional matrix indicating forbidden arcs between nodes

blacklist

if TRUE, the intra-slice arcs of the network will be learnt. If FALSE, they will be ignored

intra

additional parameters for <code><a rd-options="" href="/link/rsmax2?package=dbnR&version=0.7.1" data-mini-rdoc="dbnR::rsmax2">rsmax2</a></code> function

Learns a gaussian dynamic Bayesian network from a
dataset. It allows the creation of markovian n nets rather than only markov
1.

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.

David Quesada

dbnR

Dynamic Bayesian Network Learning and Inference

Gabriel Valverde

dmmhc function

additional parameters for <code><a rd-options='' href='rsmax2'>rsmax2</a></code> function

dmmhc: Learns the structure of a markovian n DBN model from data

Description

Usage

Arguments

Value