fold_dt_rec

This will widen the dataset to put the t previous time slices
in each row, so that it can be used to learn temporal arcs in the second
phase of the dmmhc. Recursive version not exported, the user calls from the
handler 'fold_dt'

internal

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.

David Quesada

dbnR

Dynamic Bayesian Network Learning and Inference

Gabriel Valverde

fold_dt_rec function

<dl><dt>dt</dt>
<dd>the data.table to be treated</dd>
<dt>n_prev</dt>
<dd>names of the previous time slice</dd>
<dt>size</dt>
<dd>number of time slices to unroll. Markovian 1 would be size 2</dd>
<dt>slice</dt>
<dd>the current time slice being treated. Should not be modified
when first calling.</dd></dl>

Arguments

Widens the dataset to take into account the t previous time slices — fold_dt_rec

<dl>

<dt>dt</dt>
<dd>the data.table to be treated</dd>


<dt>n_prev</dt>
<dd>names of the previous time slice</dd>


<dt>size</dt>
<dd>number of time slices to unroll. Markovian 1 would be size 2</dd>


<dt>slice</dt>
<dd>the current time slice being treated. Should not be modified
when first calling.</dd>

</dl>

fold_dt_rec: Widens the dataset to take into account the t previous time slices

Description

Usage

Value

Arguments