Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

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) .


Reference manual

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1.1-3 by Nikolaus Umlauf, a month ago

Browse source code at

Authors: Nikolaus Umlauf [aut, cre] , Nadja Klein [aut] , Achim Zeileis [aut] , Meike Koehler [ctb] , Thorsten Simon [aut] , Stanislaus Stadlmann [ctb]

Documentation:   PDF Manual  

Task views: Bayesian Inference

GPL-2 | GPL-3 license

Imports Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel

Depends on coda, colorspace, mgcv

Suggests akima, ff, ffbase, fields, gamlss, gamlss.dist, geoR, rjags, BayesX, BayesXsrc, R2BayesX, mapdata, maps, maptools, nnet, spatstat, spdep, zoo, keras, splines2, sdPrior, statmod, glogis, glmnet, scoringRules, knitr, rmarkdown, MASS

Imported by distreg.vis.

See at CRAN