Mixed-Effect Models, Particularly Spatial Models
Description
Inference based on mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Various approximations of likelihood or restricted likelihood are implemented, in particular Laplace approximation and h-likelihood (Lee and Nelder 2001 ). Both classical geostatistical models, and Markov random field models on irregular grids (as considered in the 'INLA' package, ), can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model.