deal integration

How to use the <pkg>bnlearn</pkg> package with the Bayesian
 network learning methods provided by the <pkg>deal</pkg> package.

documentation

Bayesian network structure learning (via constraint-based,
        score-based and hybrid algorithms), parameter learning (via ML
        and Bayesian estimators) and inference.  This package
        implements the Grow-Shrink (GS) algorithm, the Incremental
        Association (IAMB) algorithm, the Interleaved-IAMB (Inter-IAMB)
        algorithm, the Fast-IAMB (Fast-IAMB) algorithm, the Max-Min
        Parents and Children (MMPC) algorithm, the Hiton-PC algorithm,
        the ARACNE and Chow-Liu algorithms, the Hill-Climbing (HC)
        greedy search algorithm, the Tabu Search (TABU) algorithm, the
        Max-Min Hill-Climbing (MMHC) algorithm and the two-stage
        Restricted Maximization (RSMAX2) algorithm for both discrete
        and 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 and inference, conditional probability
        queries and cross-validation.

Marco Scutari

bnlearn

Bayesian network structure learning, parameter learning and
        inference

deal integration function

# load the bnlearn package.
> library(bnlearn)
> data(learning.test)
# learn the network structure.
> res = hc(learning.test)
> modelstring(res)
[1] "[A][C][F][B|A][D|A:C][E|B:F]"
# load the deal package.
> library(deal)Attaching package: 'deal'
 The following object(s) are masked from package:bnlearn : modelstring,
 nodes,
 score> bnlearn::node.ordering(res)
[1] "A" "C" "F" "B" "D" "E"
# create an empty network object.
> net = deal::network(learning.test[, node.ordering(res)])
# convert the bn object via its string representation.
> net = deal::as.network(bnlearn::modelstring(res), net)
# the network is the same, modulo some differences due to the
# partial ordering of the nodes.
> deal::modelstring(net)
[1] "[A][C][F][B|A][D|A:C][E|F:B]"
> bnlearn::modelstring(res)
[1] "[A][C][F][B|A][D|A:C][E|B:F]"

Export a bn object to deal

res2 = bnlearn::model2network(deal::modelstring(net))

deal integration: bnlearn - deal package integration

Description

Arguments

Export a bn object to deal

Import a network structure from deal