  We propose a comprehensive framework for additive regression models for
non-Gaussian functional responses, allowing for multiple (partially) nested or
crossed functional random effects with flexible correlation structures for,
e.g., spatial, temporal, or longitudinal functional data as well as linear and
nonlinear effects of functional and scalar covariates that may vary smoothly
over the index of the functional response. Our implementation handles
functional responses from any exponential family distribution as well as many
others like Beta- or scaled non-central $t$-distributions. Development is
motivated by and evaluated on an application to large-scale longitudinal
feeding records of pigs. Results in extensive simulation studies as well as
replications of two previously published simulation studies for generalized
functional mixed models demonstrate the good performance of our proposal. The
approach is implemented in well-documented open source software in the "pffr()"
function in R-package "refund".
