vl2bn: bnlearn interface for bayesvl objects

In bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

bnlearn interface for bayesvl objects

Description

Provides the interface to the functions in the bnlearn package for network diagnostics of an object of class bayesvl.

Usage

# Interface to bn.fit function to fit the parameters of 
# a Bayesian network conditional on its structure.
bvl_bnBayes(dag, data = NULL, method = "bayes", iss = 10, ...)

# Interface to bnlearn score function to compute the score of the Bayesian network.
bvl_bnScore(dag, data = NULL, ...)

# Interface to arc.strength function to measure the strength of the probabilistic 
# relationships expressed by the arcs of a Bayesian network.
bvl_bnStrength(dag, data = NULL, criterion = "x2", ...)

# Interface to bn.fit.barchart function to plot fit 
# the parameters of a Bayesian network conditional on its structure.
bvl_bnBarchart(dag, data = NULL, method = "bayes", iss = 10, ...) 

bvl_modelData (net, data)

bvl_compareLoo (dag1, dag2, ...)

bvl_compareWAIC (dag1, dag2, ...)

Arguments

dag	an object of class bayesvl
data	a data frame containing the variables in the model.
method	a character string, either mle for Maximum Likelihood parameter estimation or bayes for Bayesian parameter estimation (currently implemented only for discrete data).
iss	a numeric value, the imaginary sample size used by the bayes method to estimate the conditional probability tables associated with discrete nodes
criterion	a character string, the method using for measuring
net	network graph
dag1	first model to compare
dag2	second model to compare
...	extra arguments from the generic method

Value

bvl_bnScore() return a number, value of score.

Author(s)

La Viet-Phuong, Vuong Quan-Hoang

References

For documentation, case studies, worked examples, and other tutorial materials, visit the References section on our GitHub:

• https://github.com/sshpa/bayesvl/tree/master/References

For case studies using the package in research articles, see:

• https://www.nature.com/articles/s41599-020-0442-3

bayesvl documentation built on June 8, 2025, 10:38 a.m.