Bayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. 'abn' provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). The usual term to describe this model selection process is structure discovery. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from <http://www.r-inla.org>. It is recommended the testing version, which can be downloaded by running: source("http://www.math.ntnu.no/inla/givemeINLA-testing.R"). A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website.
|Author||Fraser Ian Lewis [aut], Gilles Kratzer [cre, ctb], Marta Pittavino [ctb], Reinhard Furrer [ctb]|
|Date of publication||2016-11-09 23:38:50|
|Maintainer||Gilles Kratzer <firstname.lastname@example.org>|
|License||GPL (>= 2)|
abninla-internal: abn internal functions
build_score_cache: Build a cache of goodness of fit metrics for each node in a...
dag_ex0: Synthetic validation data set for use with abn library...
dag_ex1: Synthetic validation data set for use with abn library...
dag_ex2: Synthetic validation data set for use with abn library...
dag_ex3: Validation data set for use with abn library examples
dag_ex4: Valdiation data set for use with abn library examples
dag_ex5: Valdiation data set for use with abn library examples
dag_ex6: Valdiation data set for use with abn library examples
dag_ex7: Valdiation data set for use with abn library examples
fitabn: Fit an additive Bayesian network model
mostprobable: Find most probable DAG structure
pigs.vienna: Dataset related to diseases present in 'finishing pigs',...
search_hillclimber: Find high scoring directed acyclic graphs using heuristic...
tographviz: Convert a dag into graphviz format
var33: simulated dataset from a DAG comprising of 33 variables