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.

The EFFECT statement became production. This statement is available in the HPMIXED, GLIMMIX, GLMSELECT, LOGISTIC, ORTHOREG, PHREG, PLS, QUANTREG, ROBUSTREG, SURVEYLOGISTIC, and SURVEYREG procedures.

The MCMC procedure added a RANDOM statement, which simplifies the specification of hierarchical random-effects models and significantly reduces simulation time while improving convergence.

The METHOD=FIML option in the CALIS procedure became production. This option specifies the full information maximum likelihood method.

The SURVEYPHREG procedure became production and handles time-dependent covariates.

The HPMIXED procedure added a REPEATED statement and additional covariance structures.

The MI procedure added fully conditional specification methods for multiple imputation.

The NLIN procedure was updated with features for diagnosing the nonlinear model fit.