abn: Modelling Multivariate Data with Additive Bayesian Networks
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 modeling; 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 generalized linear modeling (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 data model from interdependent variables. Laplace approximations are used to estimate the goodness of fit metrics and model parameters, and wrappers are included for the INLA package, which can be obtained from <https://www.r-inla.org>.
The computing library JAGS <https://mcmc-jags.sourceforge.io> is used to simulate 'abn'-like data.
Detailed documentation, including documented case studies, numerical accuracy/quality assurance exercises, etc., is given in Kratzer et al. (2023) <doi:10.18637/jss.v105.i08> and on the website <http://r-bayesian-networks.org>.
||R (≥ 4.0.0)
||methods, rjags, nnet, lme4, graph, Rgraphviz, doParallel, foreach
||INLA, knitr, R.rsp, testthat, entropy, moments, boot, brglm
Fraser Iain Lewis
Kalina Cherneva [ctr]
||Matteo Delucchi <matteo.delucchi at math.uzh.ch>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
||Gnu Scientific Library version >= 1.12
||abn citation info
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