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) .


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Reference manual

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install.packages("bamlss")

1.1-1 by Nikolaus Umlauf, 22 days ago


http://www.bamlss.org/


Browse source code at https://github.com/cran/bamlss


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, bit, ff, ffbase, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, R2BayesX, mapdata, maps, maptools, nnet, raster, spatstat, spdep, zoo, keras, splines2, sdPrior, glogis, glmnet, scoringRules, knitr, MASS


Imported by distreg.vis.


See at CRAN