Modelling Multivariate Data with Additive Bayesian Networks

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Documentation for package ‘abn’ version 3.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

abn.version	abn Version Information
adg	Dataset related to average daily growth performance and abattoir findings in pigs commercial production.
AIC.abnFit	Print AIC of objects of class 'abnFit'
BIC.abnFit	Print BIC of objects of class 'abnFit'
build.control	Control the iterations in 'buildScoreCache'
buildcachematrix	Documentation of C Functions
buildScoreCache	Build a cache of goodness of fit metrics for each node in a DAG, possibly subject to user-defined restrictions
buildScoreCache.bayes	Build a cache of goodness of fit metrics for each node in a DAG, possibly subject to user-defined restrictions
buildScoreCache.mle	Build a cache of goodness of fit metrics for each node in a DAG, possibly subject to user-defined restrictions
calc.node.inla.glm	Fit a given regression using INLA
calc.node.inla.glmm	Fit a given regression using INLA
Cfunctions	Documentation of C Functions
check.valid.buildControls	Simple check on the control parameters
check.valid.dag	Set of simple commonsense validity checks on the directed acyclic graph definition matrix
check.valid.data	Set of simple commonsense validity checks on the data.df and data.dists arguments
check.valid.fitControls	Simple check on the control parameters
check.valid.groups	Simple check on the grouping variable
check.valid.parents	Set of simple checks on the given parent limits
check.which.valid.nodes	Set of simple checks on the list given as parent limits
checkforcycles	Documentation of C Functions
coef.abnFit	Print coefficients of objects of class 'abnFit'
compareDag	Compare two DAGs or EGs
compareEG	Compare two DAGs or EGs
discretization	Discretization of a Possibly Continuous Data Frame of Random Variables based on their distribution
entropyData	Computes an Empirical Estimation of the Entropy from a Table of Counts
essentialGraph	Construct the essential graph
ex0.dag.data	Synthetic validation data set for use with abn library examples
ex1.dag.data	Synthetic validation data set for use with abn library examples
ex2.dag.data	Synthetic validation data set for use with abn library examples
ex3.dag.data	Validation data set for use with abn library examples
ex4.dag.data	Valdiation data set for use with abn library examples
ex5.dag.data	Valdiation data set for use with abn library examples
ex6.dag.data	Valdiation data set for use with abn library examples
ex7.dag.data	Valdiation data set for use with abn library examples
expit	expit of proportions
expit_cpp	expit function
family.abnFit	Print family of objects of class 'abnFit'
FCV	Dataset related to Feline calicivirus infection among cats in Switzerland.
fit.control	Control the iterations in 'fitAbn'
fitAbn	Fit an additive Bayesian network model
fitAbn.bayes	Fit an additive Bayesian network model
fitAbn.mle	Fit an additive Bayesian network model
fitabn_marginals	Documentation of C Functions
fit_single_node	Documentation of C Functions
forLoopContent	Build a cache of goodness of fit metrics for each node in a DAG, possibly subject to user-defined restrictions
get.var.types	Create ordered vector with integers denoting the distribution
getmarginals	Internal function called by 'fitAbn.bayes'.
infoDag	Compute standard information for a DAG.
linkStrength	Returns the strengths of the edge connections in a Bayesian Network learned from observational data
logit	Logit of proportions
logit_cpp	logit functions
logLik.abnFit	Print logLik of objects of class 'abnFit'
mb	Compute the Markov blanket
miData	Empirical Estimation of the Entropy from a Table of Counts
mostProbable	Find most probable DAG structure
mostprobable_C	Documentation of C Functions
nobs.abnFit	Print number of observations of objects of class 'abnFit'
odds	Probability to odds
or	Odds Ratio from a matrix
pigs.vienna	Dataset related to diseases present in 'finishing pigs', animals about to enter the human food chain at an abattoir.
plot.abnDag	Plots DAG from an object of class 'abnDag'
plot.abnFit	Plot objects of class 'abnFit'
plot.abnHeuristic	Plot objects of class 'abnHeuristic'
plot.abnHillClimber	Plot objects of class 'abnHillClimber'
plot.abnMostprobable	Plot objects of class 'abnMostprobable'
plotAbn	Plot an ABN graphic
print.abnCache	Print objects of class 'abnCache'
print.abnDag	Print objects of class 'abnDag'
print.abnFit	Print objects of class 'abnFit'
print.abnHeuristic	Print objects of class 'abnHeuristic'
print.abnHillClimber	Print objects of class 'abnHillClimber'
print.abnMostprobable	Print objects of class 'abnMostprobable'
regressionLoop	Fit an additive Bayesian network model
scoreContribution	Compute the score's contribution in a network of each observation.
searchHeuristic	A family of heuristic algorithms that aims at finding high scoring directed acyclic graphs
searchhill	Documentation of C Functions
searchHillClimber	Find high scoring directed acyclic graphs using heuristic search.
simulateAbn	Simulate data from a fitted additive Bayesian network.
simulateDag	Simulate a DAG with with arbitrary arcs density
skewness	Computes skewness of a distribution
summary.abnDag	Prints summary statistics from an object of class 'abnDag'
summary.abnFit	Print summary of objects of class 'abnFit'
summary.abnMostprobable	Print summary of objects of class 'abnMostprobable'
toGraphviz	Convert a DAG into graphviz format
validate_abnDag	Check for valid DAG of class 'abnDag'
validate_dists	Check for valid distribution
var33	simulated dataset from a DAG comprising of 33 variables