Package: abn
Version: 0.8
Date: 2012-10-26
Title: Data Modelling with Additive Bayesian Networks
Author: Fraser Lewis <fraseriain.lewis@uzh.ch>
Depends: R (>= 2.15.0)
Suggests: INLA, Rgraphviz, Cairo
SystemRequirements: Gnu Scientific Library version >= 1.12
Maintainer: Fraser Lewis <fraseriain.lewis@uzh.ch>
Description: Additive Bayesian network models are equivalent to
multivariate regression using graphical modelling. This library
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). 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. A
comprehensive set of documented case studies, numerical
accuracy/quality assurance exercises, and additional
documentation are available from the abn website.
License: GPL (>= 2)
LazyData: true
BuildVignettes: no
URL: http://www.r-bayesian-networks.org
Packaged: 2012-10-26 12:36:25 UTC; fraser
Repository: CRAN
Date/Publication: 2012-10-26 13:17:26

Package: abn
Version: 0.82
Date: 2012-11-02
Title: Data Modelling with Additive Bayesian Networks
Author: Fraser Lewis <fraseriain.lewis@uzh.ch>
Depends: R (>= 2.15.0)
Suggests: INLA, Rgraphviz, Cairo
SystemRequirements: Gnu Scientific Library version >= 1.12
Maintainer: Fraser Lewis <fraseriain.lewis@uzh.ch>
Description: Additive Bayesian network models are equivalent to
Bayesian multivariate regression using graphical modelling.
This library 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). 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 library. A comprehensive set of documented
case studies, numerical accuracy/quality assurance exercises,
and additional documentation are available from the abn
website.
License: GPL (>= 2)
LazyData: true
BuildVignettes: no
URL: http://www.r-bayesian-networks.org
Packaged: 2013-01-04 08:45:19 UTC; fraser
Repository: CRAN
Date/Publication: 2013-01-04 10:58:19