Integrative Bayesian Multiple Regression for Multi-Platform Biomarkers // A Bayesian framework that integrates several regression models to identify a parsimonious set of biomarkers shared across disparate data platforms (for example genomic, transcriptomic and proteomic assays). Subjects are partitioned into subgroups defined by their pattern of platform availability, so that no subject with partially missing platform data is excluded, and information is borrowed across subgroups through a Markov random field prior on the variable- selection indicators together with non-local (product moment) priors on the regression effects. The methodology was introduced for time-to-event outcomes by Chekouo, Stingo, Doecke and Do (2017) doi:10.1111/biom.12587; this package additionally supports continuous (Gaussian) and binary (probit) outcomes. Posterior inference is carried out by a Markov chain Monte Carlo sampler implemented in C for computational efficiency.