local discovery algorithms

aracne

chow.liu

ARACNE and Chow-Liu learn simple network structures from data using pairwise
 mutual information coefficients.

local learning

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

local discovery algorithms function

<dl> <dt>x</dt>
<dd>a data frame containing the variables in the model.</dd> <dt>whitelist</dt>
<dd>a data frame with two columns (optionally labelled "from"
 and "to"), containing a set of arcs to be included in the graph.</dd> <dt>blacklist</dt>
<dd>a data frame with two columns (optionally labelled "from"
 and "to"), containing a set of arcs not to be included in the graph.</dd> <dt>mi</dt>
<dd>a character string, the estimator used for the pairwise (that is,
 unconditional) mutual information coefficients in the ARACNE and Chow-Liu
 algorithms. Possible values are <code>"mi"</code> (discrete mutual information)
 and <code>"mi-g"</code> (Gaussian mutual information).</dd> <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code>, a lot of debugging output is
 printed. Otherwise, the function is completely silent.</dd></dl>

Arguments

Author

ARACNE and Chow-Liu learn simple network structures from data using pairwise
 mutual information coefficients.

Local discovery structure learning algorithms — local discovery algorithms

<dl>

 <dt>x</dt>
<dd>a data frame containing the variables in the model.</dd>

 <dt>whitelist</dt>
<dd>a data frame with two columns (optionally labelled "from"
 and "to"), containing a set of arcs to be included in the graph.</dd>

 <dt>blacklist</dt>
<dd>a data frame with two columns (optionally labelled "from"
 and "to"), containing a set of arcs not to be included in the graph.</dd>

 <dt>mi</dt>
<dd>a character string, the estimator used for the pairwise (that is,
 unconditional) mutual information coefficients in the ARACNE and Chow-Liu
 algorithms. Possible values are <code>"mi"</code> (discrete mutual information)
 and <code>"mi-g"</code> (Gaussian mutual information).</dd>

 <dt>debug</dt>
<dd>a boolean value. If <code>TRUE</code>, a lot of debugging output is
 printed. Otherwise, the function is completely silent.</dd>

</dl>

local discovery algorithms: Local discovery structure learning algorithms

Description

Usage

Value

Arguments

Author

See Also