A flexible approach to finite mixture regression models for multivariate mixed responses

We describe regression models for multivariate mixed responses, where association between outcomes is modeled through discrete, outcome-specific, latent effects, accounting for heterogeneity and dependence. We relax the standard unidimensionality hypothesis, and adopt a multidimensional latent class approach, with possibly different numbers of locations in each margin, and a full association structure.