learning.test

This a synthetic data set used as a test case in the bnlearn package.

datasets

Bayesian network structure learning, parameter learning and
inference.
This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
and Tabu Search) and hybrid (MMHC and RSMAX2) 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 and
cross-validation. Development snapshots with the latest bugfixes are
available from <http://www.bnlearn.com>.

Marco Scutari

bnlearn

Bayesian Network Structure Learning, Parameter Learning and
Inference

learning.test function

The <code>learning.test</code> data set contains the following variables:<ul>
<li><code>A</code>, a three-level factor with levels <code>a</code>, <code>b</code> and
 <code>c</code>.</li>
<li><code>B</code>, a three-level factor with levels <code>a</code>, <code>b</code> and
 <code>c</code>.</li>
<li><code>C</code>, a three-level factor with levels <code>a</code>, <code>b</code> and
 <code>c</code>.</li>
<li><code>D</code>, a three-level factor with levels <code>a</code>, <code>b</code> and
 <code>c</code>.</li>
<li><code>E</code>, a three-level factor with levels <code>a</code>, <code>b</code> and
 <code>c</code>.</li>
<li><code>F</code>, a two-level factor with levels <code>a</code> and <code>b</code>.</li>
</ul>

learning.test: Synthetic (discrete) data set to test learning algorithms

Description

Usage

Arguments

Format

Examples