  The standard procedures for analysing hierarquical or grouped data are by
(non)linear mixed models or generalized mixed models. However, the generalized
additive models for location, scale and shape (GAMLSSs) also allow different
types of random effects to be included in the model formulation. Even though
already popular in many areas of research, this type of models have not been
found to be used for mixed modeling purposes yet. Therefore, this paper
describes the analysis of an experiment with plants' growth using mixed
GAMLSSs, comparing it to a linear mixed model approach.
