bamlss: 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 (2018) <doi:10.1080/10618600.2017.1407325>.

Version: 1.0-2
Depends: R (≥ 3.2.3), coda, colorspace, mgcv
Imports: Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel
Suggests: akima, bit, ff, ffbase, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, mapdata, maps, maptools, raster, spatstat, spdep, zoo, keras, splines2, sdPrior, glogis, glmnet, scoringRules
Published: 2019-07-30
Author: Nikolaus Umlauf [aut, cre], Nadja Klein [aut], Achim Zeileis ORCID iD [aut], Meike Koehler [ctb], Thorsten Simon ORCID iD [aut], Stanislaus Stadlmann [ctb]
Maintainer: Nikolaus Umlauf <Nikolaus.Umlauf at>
License: GPL-2 | GPL-3
NeedsCompilation: yes
Citation: bamlss citation info
Materials: NEWS
In views: Bayesian
CRAN checks: bamlss results


Reference manual: bamlss.pdf
Package source: bamlss_1.0-2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: bamlss_1.0-2.tgz, r-oldrel: bamlss_1.0-2.tgz
Old sources: bamlss archive

Reverse dependencies:

Reverse imports: distreg.vis


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