dsep

Check whether two nodes are d-separated.

graphs

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

dsep function

<dl> <dt>bn</dt>
<dd>an object of class <code>bn</code>.</dd> <dt>x,y</dt>
<dd>a character string, the label of a node.</dd> <dt>z</dt>
<dd>an optional vector of character strings, the label of the
 (candidate) d-separating nodes. It defaults to the empty set.</dd></dl>

Arguments

Author

Check whether two nodes are d-separated.

Test d-separation — dsep

<dl>

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

 <dt>x,y</dt>
<dd>a character string, the label of a node.</dd>

 <dt>z</dt>
<dd>an optional vector of character strings, the label of the
 (candidate) d-separating nodes. It defaults to the empty set.</dd>

</dl>

dsep: Test d-separation

Description

Usage

Value

Arguments

Author

References

Examples