# Modelling Multivariate Data with Additive Bayesian Networks

## Documentation for package ‘abn’ version 3.1.13

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

AIC.abnFit | Print AIC of objects of class 'abnFit'
as.data.frame.abnDag | Transform the adjacency matrix representation of a DAG to a data.frame with columns "from" and "to" representing directed edges.
BIC.abnFit | Print BIC of objects of class 'abnFit'
build.control | Control the iterations in 'buildScoreCache'
check.valid.fitControls | Simple check on the control parameters
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
expit | expit of proportions
expit_cpp | expit function
export_abnFit | Export abnFit object to structured JSON format
family.abnFit | Print family of objects of class 'abnFit'
fit.control | Control the iterations in 'fitAbn'
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
nobs.abnFit | Print number of observations of objects of class 'abnFit'
odds | Probability to odds
or | Odds Ratio from a matrix
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'
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'
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
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