calc_sigma_cpp

Calculate the sigma covariance matrix of a Gaussian linear network. 
This is the C++ backend of the function.

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

calc_sigma_cpp function

<dl><dt>fit</dt>
<dd>a bn.fit object as a Rcpp::List</dd>
<dt>order</dt>
<dd>a topological ordering of the nodes as a vector of strings</dd></dl>

Arguments

Calculate the sigma covariance matrix of a Gaussian linear network. 
This is the C++ backend of the function. — calc_sigma_cpp

<dl>

<dt>fit</dt>
<dd>a bn.fit object as a Rcpp::List</dd>


<dt>order</dt>
<dd>a topological ordering of the nodes as a vector of strings</dd>

</dl>

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calc_sigma_cpp: Calculate the sigma covariance matrix of a Gaussian linear network. This is the C++ backend of the function.

Description

Usage

Value

Arguments