Bayesian Additive Models for Location, Scale, and Shape (and
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
and the R package in Umlauf, Klein, Simon, Zeileis (2019)