bamlss

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) doi:10.1080/10618600.2017.1407325 and the R package in Umlauf, Klein, Simon, Zeileis (2021) doi:10.18637/jss.v100.i04.