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 | |

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 |