PROC NLMIXED handles models in which the fixed or random effects enter nonlinearly. It requires that you specify a conditional distribution of the data given the random effects, with available distributions including the normal, binomial, and Poisson. You can alternatively code your own distribution with SAS programming statements. Under a normality assumption for the random effects, PROC NLMIXED performs maximum likelihood estimation via adaptive Gaussian quadrature and a dual quasi-Newton optimization algorithm. Besides standard maximum likelihood results, you can obtain empirical Bayes predictions of the random effects and estimates of arbitrary functions of the parameters with delta-method standard errors. PROC NLMIXED has a wide variety of applications; two of the most common applications are nonlinear growth curves and overdispersed binomial data.