configs

Create configurations of discrete variables, which are 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, MMPC,
Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing,
Tabu Search, DirectLiNGAM) and hybrid (MMHC, RSMAX2, H2PC) structure learning
algorithms for discrete, Gaussian, conditional Gaussian and zero-inflated
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, interventions,
counterfactuals, 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 configurations are included as
 levels in the return value. Otherwise, only configurations that are actually
 observed are considered.</dd></dl>

Arguments

Author

Create configurations of discrete variables, which are 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 configurations are included as
 levels in the return value. Otherwise, only configurations that are actually
 observed are considered.</dd>

</dl>

configs: Construct configurations of discrete variables

Description

Usage

Value

Arguments

Author

Examples