Description: A graphical modelling formulation is used to construct Bayesian regression models for analyses of multivariate data. The models comprise of directed acyclic graphs where each node in the graph comprises a generalized linear model, and a greedy search heuristic is used to identify high scoring models that attempt to identify relationships between all variables in the data. Currently implemented are models for data comprising of categorical (Binomial with logit link) and/or continuous (Gaussian with identity link) variables. Laplace approximations are used to estimate marginal likelihoods and compute marginal posterior densities.

Package: abn
Version: 0.7
Date: 2012-04-18
Title: Data Modelling with Additive Bayesian Networks
Author: Fraser Lewis <fraseriain.lewis@uzh.ch>
SystemRequirements: Gnu Scientific Library version >= 1.12
Maintainer: Fraser Lewis <fraseriain.lewis@uzh.ch>
Description: This library provides computational routines to help
determine optimal Bayesian Network models for a given data set,
where these models are used to identify all statistical
dependencies present in messy, complex data. The usual term
used to describe this model selection process is structure
discovery, or more generally, data mining. Currently, a
standard heuristic search and order based exact search are
implemented, across two different types of Bayesian Network
model: i) the classical (conjugate) contingency formulation for
observations comprising of binary or multinomial variables; and
ii) an additive formulation where each node in the network is
modelled by a generalised linear regression model and this
formulation applies to observations comprising of binary and/or
Gaussian variables, where a logistic link function is used in
the former. The additive formulation is analogous to searching
for the most appropriate multidimensional Bayesian regression
model of the data.
License: GPL (>= 2)
LazyData: true
BuildVignettes: no
URL: http://www.vetepi.uzh.ch/research/bgm.html
Packaged: 2012-04-19 05:41:34 UTC; fraser
Repository: CRAN
Date/Publication: 2012-07-11 10:54:30