The experimental FMM procedure fits statistical models to data where the distribution of the response is a finite mixture of univariate distributions. These models are useful for applications such as estimating multimodal or heavy-tailed densities, fitting zero-inflated or hurdle models to count data with excess zeros, modeling overdispersed data, and fitting regression models with complex error distributions.

PROC FMM fits finite mixtures of regression models or finite mixtures of generalized linear models in which the regression structure and the covariates can be the same across components or different. Maximum likelihood and Bayesian methods are available with the FMM procedure.