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Over the last decades, the challenges in applied regression and in predictivemodeling have been changing considerably: (1) More flexible modelspecifications are needed as big(ger) data become available, facilitated bymore powerful computing infrastructure. (2) Full probabilistic modeling ratherthan predicting just means or expectations is crucial in many applications. (3)Interest in Bayesian inference has been increasing both as an appealingframework for regularizing or penalizing model estimation as well as a naturalalternative to classical frequentist inference. However, while there has been alot of research in all three areas, also leading to associated softwarepackages, a modular software implementation that allows to easily combine allthree aspects has not yet been available. For filling this gap, the R packagebamlss is introduced for Bayesian additive models for location, scale, andshape (and beyond). At the core of the package are algorithms forhighly-efficient Bayesian estimation and inference that can be applied togeneralized additive models (GAMs) or generalized additive models for location,scale, and shape (GAMLSS), also known as distributional regression. However,its building blocks are designed as "Lego bricks" encompassing variousdistributions (exponential family, Cox, joint models, ...), regression terms(linear, splines, random effects, tensor products, spatial fields, ...), andestimators (MCMC, backfitting, gradient boosting, lasso, ...). It isdemonstrated how these can be easily recombined to make classical models moreflexible or create new custom models for specific modeling challenges.

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)