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 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 library. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the abn website.