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 (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

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