pcalg integration

as.bn.pcAlgo

Convert <code>pcAlgo</code> objects to <code>bn</code> objects.

utilities

interfaces to other packages

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

pcalg integration function

<dl> <dt>x</dt>
<dd>an object of class <code>pcAlgo</code>.</dd> <dt>...</dt>
<dd>extra arguments from the generic method (currently ignored).</dd> <dt>check.cycles</dt>
<dd>a boolean value. If <code>FALSE</code> the returned network will
 not be checked for cycles.</dd></dl>

Arguments

Author

Convert <code>pcAlgo</code> objects to <code>bn</code> objects.

Import and export networks from the pcalg package — pcalg integration

<dl>

 <dt>x</dt>
<dd>an object of class <code>pcAlgo</code>.</dd>

 <dt>...</dt>
<dd>extra arguments from the generic method (currently ignored).</dd>

 <dt>check.cycles</dt>
<dd>a boolean value. If <code>FALSE</code> the returned network will
 not be checked for cycles.</dd>

</dl>

pcalg integration: Import and export networks from the pcalg package

Description

Usage

Value

Arguments

Author