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>. The computing library JAGS < http://mcmc-jags.sourceforge.net/> is used to simulate 'abn'-like data. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the 'abn' website < http://r-bayesian-networks.org>.

- abn/ contains source code
- Use testthat for tests

- bug correction for simulation and prediction
- plot function with mb, fitabn output
- implementation of forumla in: Buildscore/fitabn (modification of rd file)
- plot function + rd file
- prediction function + rd file
- simulation function + rd file

- rd file: plot, simulation, prediction
- implementation of test for plot/prediction/simulation functions
- diagnostic plots
- bootstrapping
- Implement parallel computing in fitabn(), buildscorecache()

- Implement new prior MP (multinomial + data separation)
- Data separation
- Random effect (already one layer)
- Multinomial
- Negative binomial, ...
- Time/Memory resources optimization
- Handling missing data