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 (2017) <doi:10.1080/10618600.2017.1407325>.

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Description file content

Package
bamlss
Version
1.0-1
Date
2018-10-12
Title
Bayesian Additive Models for Location Scale and Shape (and Beyond)
Description
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 (2017) .
Depends
R (>= 3.2.3), coda, colorspace, mgcv
Imports
Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel
Suggests
akima, bit, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, mapdata, maps, maptools, raster, spatstat, spdep, zoo, keras, splines2, sdPrior, glogis, glmnet, scoringRules
License
GPL-2 | GPL-3
LazyLoad
yes
NeedsCompilation
yes
Packaged
2018-10-12 07:51:05 UTC; nik
Author
Nikolaus Umlauf [aut, cre], Nadja Klein [aut], Achim Zeileis [aut] (), Meike Koehler [aut], Thorsten Simon [ctb], Stanislaus Stadlmann [ctb]
Maintainer
Nikolaus Umlauf
Repository
CRAN
Date/Publication
2018-10-12 08:40:03 UTC

install.packages('bamlss')

1.0-1

a month ago

Nikolaus Umlauf

GPL-2 | GPL-3

Depends on

R (>= 3.2.3), coda, colorspace, mgcv

Imports

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

Suggests

akima, bit, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, mapdata, maps, maptools, raster, spatstat, spdep, zoo, keras, splines2, sdPrior, glogis, glmnet, scoringRules

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