Mixed-Effect Models, Particularly Spatial Models

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, < https://www.r-inla.org>), can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model.