shift_values

This function moves the values in t_0, t_1, ..., t_n-1 in a folded dataset row to
t_1, t_2, ..., t_n. All the variables in t_0 will be inputed with NAs and the
obtained row can be used to forecast up to any desired point.

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

shift_values function

<dl><dt>f_dt</dt>
<dd>a folded dataset</dd>
<dt>row</dt>
<dd>the index of the row that is going to be processed</dd></dl>

Arguments

Move the window of values backwards in a folded dataset row — shift_values

<dl>

<dt>f_dt</dt>
<dd>a folded dataset</dd>


<dt>row</dt>
<dd>the index of the row that is going to be processed</dd>

</dl>

shift_values: Move the window of values backwards in a folded dataset row

Description

Usage

Value

Arguments

Examples