natCauslist

Constructor of the 'natCauslist' class

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

natCauslist function

<dl><dt>ordering</dt>
<dd>a vector with the names of the nodes in t_0</dd>
<dt>ordering_raw</dt>
<dd>a vector with the names of the nodes without the appended "_t_0"</dd></dl>

Arguments

<dl>
<dt><code>cl</code></dt>
<dd>List of causal units</dd></dl>
<dt><code>ordering</code></dt>
<dd>String vector defining the order of the nodes in t_0</dd>
<dt><code>ordering</code></dt>
<dd>String vector defining the order of the nodes without the appended "_t_0"</dd>

Fields

R6 class that defines causal lists in the PSO — natCauslist

<dl>

<dt>ordering</dt>
<dd>a vector with the names of the nodes in t_0</dd>


<dt>ordering_raw</dt>
<dd>a vector with the names of the nodes without the appended "_t_0"</dd>

</dl>

<dl>
<dt><code>cl</code></dt>
<dd>List of causal units</dd>


<dt><code>ordering</code></dt>
<dd>String vector defining the order of the nodes in t_0</dd>


<dt><code>ordering</code></dt>
<dd>String vector defining the order of the nodes without the appended "_t_0"</dd>


</dl>

natCauslist: R6 class that defines causal lists in the PSO

Description

Value

Arguments

Fields

Details