BGLIMM Procedure
The BGLIMM procedure is a highperformance, samplingbased procedure that provides Bayesian inference for generalized linear mixed models (GLMMs). PROC BGLIMM uses syntax similar to that of
PROC MIXED and PROC GLIMMIX in specifying a GLMM. The following are highlights of the BGLIMM procedure's features:
 GLMMs with univariate or multivariate random effects
 nested or nonnested hierarchical models
 repeated measurements models (balanced or unbalanced data) with normal data
 suite of covariance structures for random effects and residuals
 builtin prior distributions for regression coefficients and covariance parameters
 model heterogeneity in covariance structures
 produce estimate and credible intervals for estimate linear combination of effects
 support for missing completely at random (MCAR) and missing at random (MAR) approaches in modeling missing data

 works with the postprocessing autocall macros that are designed for Bayesian posterior samples
 provides a variety of Markov chain convergence diagnostics
 creates an output data set that contains the posterior samples of all parameters
 supports a CLASS statement for specifying classification variables
 supports BY group processing
 automatically produces graphs by using ODS Graphics
 multithreaded

For further details see the SAS/STAT User's Guide: The BGLIMM Procedure ( PDF  HTML )
Examples