Mixed Models, Particularly Spatial GLMMs
Description
Implements a collection of functions for inference in mixed models. It was developed in particular for GLMMs with spatial correlations, but also fits models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroskedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.