The HPFMM Procedure

maximum likelihood estimation for all models

Markov chain Monte Carlo estimation for many models, including zero-inflated Poisson models

many built-in link and distribution functions for modeling, including the beta, shifted t, Weibull, beta-binomial, and generalized Poisson distributions, in addition to many standard members of the exponential family of distributions

specialized built-in mixture models such as the binomial cluster model (Morel and Nagaraj, 1993; Morel and Neerchal, 1997; Neerchal and Morel, 1998)

acceptance of multiple MODEL statements to build mixture models in which the model effects, distributions, or link functions vary across mixture components

model-building syntax using CLASS and effect-based MODEL statements familiar from many other SAS/STAT procedures (for example, the GLM, GLIMMIX, and MIXED procedures)

evaluation of sequences of mixture models when you specify ranges for the number of components

simple syntax to impose linear equality and inequality constraints among parameters

ability to model regression and classification effects in the mixing probabilities through the PROBMODEL statement

ability to incorporate full or partially known component membership into the analysis through the PARTIAL= option in the PROC HPFMM statement

OUTPUT statement that produces a SAS data set with important statistics for interpreting mixture models, such as component log likelihoods and prior and posterior probabilities

ability to add zero-inflation to any model

output data set with posterior parameter values for the Markov chain

multithreading and distributed computing for high-performance optimization and Monte Carlo sampling

enables you to run in distributed mode on a cluster of machines that distribute the data and the computations

enables you to run in single-machine mode on the server where SAS is installed

exploits all the available cores and concurrent threads, regardless of execution mode