Man pages for abn
Modelling Multivariate Data with Additive Bayesian Networks
| abn-package | 'abn' Package |
| abn.version | abn Version Information |
| adg | Dataset related to average daily growth performance and... |
| AIC.abnFit | Print AIC of objects of class 'abnFit' |
| bern_bugs | Bugs code for Bernoulli response |
| BIC.abnFit | Print BIC of objects of class 'abnFit' |
| build.control | Control the iterations in 'buildScoreCache' |
| buildScoreCache | Build a cache of goodness of fit metrics for each node in a... |
| calc.node.inla.glm | Fit a given regression using INLA |
| calc.node.inla.glmm | Fit a given regression using INLA |
| categorical_bugs | Bugs code for Categorical response |
| 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... |
| check.valid.data | Set of simple commonsense validity checks on the data.df and... |
| 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 |
| coef.abnFit | Print coefficients of objects of class 'abnFit' |
| compareDag | Compare two DAGs or EGs |
| compareEG | Compare two DAGs or EGs |
| createAbnDag | Make DAG of class "abnDag" |
| discretization | Discretization of a Possibly Continuous Data Frame of Random... |
| dot-onAttach | Prints start up message |
| entropyData | Computes an Empirical Estimation of the Entropy from a Table... |
| essentialGraph | Construct the essential graph |
| eval.across.grid | function to get marginal across an equal grid |
| ex0.dag.data | Synthetic validation data set for use with abn library... |
| ex1.dag.data | Synthetic validation data set for use with abn library... |
| ex2.dag.data | Synthetic validation data set for use with abn library... |
| 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 |
| factorial | Factorial |
| factorial_fast | Fast Factorial |
| family.abnFit | Print family of objects of class 'abnFit' |
| FCV | Dataset related to Feline calicivirus infection among cats in... |
| find.next.left.x | Find next X evaluation Point |
| fitAbn | Fit an additive Bayesian network model |
| fit.control | Control the iterations in 'fitAbn' |
| forLoopContentFitBayes | Regress each node on its parents.#' |
| formula_abn | Formula to adjacency matrix |
| g2b2c_data | Toy Data Set for Examples in README |
| g2pbcgrp | Toy Data Set for Examples in README |
| gauss_bugs | Bugs code for Gaussian response |
| getmarginals | Internal function called by 'fitAbn.bayes'. |
| getMargsINLA | function to extract marginals from INLA output |
| getModeVector | function to extract the mode from INLA output |
| getMSEfromModes | Extract Standard Deviations from all Gaussian Nodes |
| get.quantiles | function to extract quantiles from INLA output |
| get.var.types | Create ordered vector with integers denoting the distribution |
| infoDag | Compute standard information for a DAG. |
| irls_binomial_cpp | Iterative Reweighed Least Square algorithm for Binomials |
| irls_binomial_cpp_br | BR Iterative Reweighed Least Square algorithm for Binomials |
| irls_binomial_cpp_fast | Fast Iterative Reweighed Least Square algorithm for Binomials |
| irls_binomial_cpp_fast_br | Fast Br Iterative Reweighed Least Square algorithm for... |
| irls_gaussian_cpp | Iterative Reweighed Least Square algorithm for Gaussians |
| irls_gaussian_cpp_fast | Fast Iterative Reweighed Least Square algorithm for Gaussians |
| irls_poisson_cpp | Iterative Reweighed Least Square algorithm for Poissons |
| irls_poisson_cpp_fast | Fast Iterative Reweighed Least Square algorithm for Poissons |
| linkStrength | Returns the strengths of the edge connections in a Bayesian... |
| logit | Logit of proportions |
| logit_cpp | logit functions |
| logLik.abnFit | Print logLik of objects of class 'abnFit' |
| makebugs | Make BUGS model from fitted DAG |
| makebugsGroup | Make BUGS model from fitted DAG with grouping |
| mb | Compute the Markov blanket |
| mi_cpp | Mutual Information |
| 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 |
| pigs.vienna | Dataset related to diseases present in 'finishing pigs',... |
| plotAbn | Plot an ABN graphic |
| 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' |
| pois_bugs | Bugs code for Poisson response |
| 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' |
| rank_cpp | Rank of a matrix |
| scoreContribution | Compute the score's contribution in a network of each... |
| searchHeuristic | A family of heuristic algorithms that aims at finding high... |
| searchHillClimber | Find high scoring directed acyclic graphs using heuristic... |
| simulateAbn | Simulate data from a fitted additive Bayesian network. |
| simulateDag | Simulate a DAG with with arbitrary arcs density |
| skewness | Computes skewness of a distribution |
| std.area.under.grid | Standard Area Under the Marginal |
| strsplits | Recursive string splitting |
| 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' |
| tidy.cache | tidy up cache |
| toGraphviz | Convert a DAG into graphviz format |
| validate_dists | Check for valid distribution |
| var33 | simulated dataset from a DAG comprising of 33 variables |
