Modelling Multivariate Data with Additive Bayesian Networks


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Documentation for package ‘abn’ version 1.3

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buildscorecache 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 based on Information Theoretic for each node in a DAG, possibly subject to user defined restrictions
compareDag Compare two DAGs
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 Plot an ABN graphic
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, Logit and odds
expit_cpp logit and logit functions
fitabn Fit an additive Bayesian network model
fitabn.mle Fit an additive Bayesian network model based on maximum likelihood estimation.
infoDag Compute standard information for a DAG.
link.strength A function that returns the strengths of the edge connections in a Bayesian Network learned form observational data.
logit Expit, Logit and odds
logit_cpp logit and logit functions
mb Compute the Markov blanket
miData Computes an Empirical Estimation of the Entropy from a Table of Counts
mostprobable Find most probable DAG structure
odds Expit, Logit and odds
or Odd ratio from a table
pigs.vienna Dataset related to diseases present in 'finishing pigs', animals about to enter the human food chain at an abattoir.
plotabn Plot an ABN graphic
search.heuristic A familly of heuristic algorithms that aims at finding high scoring directed acyclic graphs
search.hillclimber Find high scoring directed acyclic graphs using heuristic search.
simulateabn Simulate from an ABN network
simulateDag Simulate DAGs
tographviz Convert a dag into graphviz format
var33 simulated dataset from a DAG comprising of 33 variables