 provides the following builtin link functions:
 cumulative complementary loglog
 cumulative logit
 cumulative loglog
 cumulative probit
 complementary loglog
 generalized logit
 identity
 log
 logit
 loglog
 probit
 power with exponent λ = number
 power with exponent 2
 reciprocal
 provides the following builtin distributions and associated variance functions:
 beta
 binary
 binomial
 exponential
 gamma
 normal
 geometric
 inverse gaussian
 lognormal
 negative binomial
 Poisson
 t
 use SAS programming statements within the procedure to compute model effects,
weights, frequency, subject, group, and other variables, and to define mean
and variance functions
 fits covariance structures including:
 ANTE(1)
 AR(1)
 ARH(1)
 ARMA(1,1)
 Cholesky
 compound symmetry
 heterogeneous compound symmetry
 factor analytic
 HuynhFeldt
 general linear
 Pspline
 radial smoother
 simple
 exponential spatial
 gaussian
 Matern
 power
 anisitropic power
 spherical
 Toeplitz
 unstructured
 permits subject and group effects that enable blocking and heterogeneity, respectively
 permits weighted multilevel models for analyzing survey data that arise from multistage sampling
 choice of linearization approach or integral approximation by quadrature or Laplace method
for mixed models with nonlinear random effects or nonnormal distribution
 choice of linearization about expected values or expansion about current solutions of best
linear unbiased predictors (BLUP)

 flexible covariance structures for random and residual random effects, including variance
components, unstructured, autoregressive, and spatial structures
 produce hypothesis tests and estimable linear combinations of effects
 provides a mechanism to obtain inferences for the covariance parameters.
Significance tests are based on the ratio of (residual) likelihoods or pseudolikelihoods.
Confidence limits and bounds are computed as Wald or likelihood ratio limits.
 construct special collections of columns for the design matrices in your model.
These special collections, which are referred to as constructed effects
can include the following:
 COLLECTION is a collection effect defining one or more variables as a single effect
with multiple degrees of freedom. The variables in a collection are
considered as a unit for estimation and inference.
 MULTIMEMBER  MM is a multimember classification effect whose levels are determined
by one or more variables that appear in a CLASS statement.
 POLYNOMIAL  POLY is a multivariate polynomial effect in the specified numeric variables.
 SPLINE is a regression spline effect whose columns are univariate spline expansions
of one or more variables. A spline expansion replaces the
original variable with an expanded or larger set of new variables.
 provides the following estimation methods:
 RSPL
 MSPL
 RMPL
 MMPL
 Laplace
 adaptive quadrature
 enables you to exercise control over the numerical optimization.
You can choose techniques, update methods, line search algorithms, convergence criteria,
and more. Or, you can choose the default optimization strategies selected for the particular
class of model you are fitting.
 enables you to generate variables with SAS programming statements inside of PROC GLIMMIX (except
for variables listed in the CLASS statement).
 performs grouped data analysis
 supports BY group processing, which enebales you to obtain separate analyses on grouped observations
 use ODS to create a SAS data set corresponding to any table
 automaticlly generates graphs by using ODS Graphics
