configs

Create configurations of discrete variables, which can be used in modelling
 conditional probability tables.

convenience functions

data preprocessing

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

Tomi Silander

configs function

<dl> <dt>data</dt>
<dd>a data frame containing factor columns.</dd> <dt>all</dt>
<dd>a boolean value. If <code>TRUE</code> all configuration are included as
 levels in the return value; otherwise only configurations which are actually
 observed are considered.</dd></dl>

Arguments

Author

Create configurations of discrete variables, which can be used in modelling
 conditional probability tables.

Construct configurations of discrete variables — configs

<dl>

 <dt>data</dt>
<dd>a data frame containing factor columns.</dd>

 <dt>all</dt>
<dd>a boolean value. If <code>TRUE</code> all configuration are included as
 levels in the return value; otherwise only configurations which are actually
 observed are considered.</dd>

</dl>

configs: Construct configurations of discrete variables

Description

Usage

Value

Arguments

Author

Examples