# Modelling Multivariate Data with Additive Bayesian Networks

## Documentation for package ‘abn’ version 3.0.2

- 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
g2b2c_data | Toy Data Set for Examples in README
g2pbcgrp | Toy Data Set for Examples in README
get.var.types | Create ordered vector with integers denoting the distribution
getmarginals | Internal function called by 'fitAbn.bayes'.
getMSEfromModes | Extract Standard Deviations from all Gaussian Nodes
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
modes2coefs | Convert modes to fitAbn.mle$coefs structure
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