node ordering utilities

node.ordering

an object of class <code>bn</code> or <code>bn.fit</code>.

a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.

debug

Find the partial node ordering implied by a network.

graphs

convenience functions

Bayesian network structure learning, parameter learning and inference.
This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
Gaussian and conditional Gaussian networks, along with many score functions and
conditional independence tests.
The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
Some utility functions (model comparison and manipulation, random data generation, arc
orientation testing, simple and advanced plots) are included, as well as support for
parameter estimation (maximum likelihood and Bayesian) and inference, conditional
probability queries, cross-validation, bootstrap and model averaging.
Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

node ordering utilities function

<dl> <dt>x</dt>
<dd>an object of class <code>bn</code> or <code>bn.fit</code>.</dd> <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.</dd></dl>

Arguments

Author

Find the partial node ordering implied by a network.

Partial node orderings — node ordering utilities

<dl>

 <dt>x</dt>
<dd>an object of class <code>bn</code> or <code>bn.fit</code>.</dd>

 <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code> a lot of debugging output is
 printed; otherwise the function is completely silent.</dd>

</dl>

node ordering utilities: Partial node orderings

Description

Usage

Value

Arguments

Author

Examples