  Studies that collect multi-outcome data such as tobacco and alcohol use are
becoming increasingly common. In principle, multi-outcomes studies investigate
the correlations between outcomes, including, causal links and/or joint
distributions. Although there are many methods for studying multivariate
outcomes, significant limitations regarding scale and interpretation persist.
Here we introduce a model based on the exponential-family for discrete binary
outcomes that provides a flexible framework for hypothesis testing of multiple
binary outcomes in a computationally efficient fashion.
