  For an observed response that is composed by a set - or vector - of positive
values that sum up to 1, the Dirichlet distribution (Bol'shev, 2018) is a
helpful mathematical construction for the quantification of the data-generating
mechanics underlying this process. In applications, these response-sets are
usually denoted as proportions, or compositions of proportions, and by means of
covariates, one wishes to manifest the underlying signal - by changes in the
value of these covariates - leading to differently distributed response
compositions. This article gives a brief introduction into this class of
regression models, and based on a recently developed formulation (Maier, 2014),
illustrates the implementation in the Bayesian inference framework Stan.
