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)

person(given = "Meike", family = "Koehler", role = "aut", email = "Meike.Koehler@helmholtz-muenchen.de"),
person(given = "Thorsten", family = "Simon", role = "ctb", email = "thorsten.simon@uibk.ac.at"))
Imports: Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel
mapdata, maps, maptools, raster, spatstat, spdep, zoo, keras,

person(given = "Thorsten", family = "Simon", role = "ctb", email = "thorsten.simon@uibk.ac.at"), person(given = "Stanislaus", family = "Stadlmann", role = "ctb", email = "stadlmann@uni-goettingen.de"))
splines2, sdPrior, glogis, glmnet, scoringRules

person(given = "Meike", family = "Koehler", role = "ctb", email = "meike_koehler@gmx.de"),
person(given = "Thorsten", family = "Simon", role = "aut", email = "thorsten.simon@uibk.ac.at",
person(given = "Stanislaus", family = "Stadlmann", role = "ctb", email = "stadlmann@uni-goettingen.de"))
The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2018)
Suggests: akima, bit, ff, ffbase, fields, gamlss, geoR, rjags, BayesX,
BayesXsrc, mapdata, maps, maptools, raster, spatstat, spdep,
zoo, keras, splines2, sdPrior, glogis, glmnet, scoringRules

Title: Bayesian Additive Models for Location, Scale, and Shape (and
The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
<doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2019)
Depends: R (>= 3.5.0), coda, colorspace, mgcv
BayesXsrc, R2BayesX, mapdata, maps, maptools, raster, spatstat,
spdep, zoo, keras, splines2, sdPrior, glogis, glmnet,

BayesXsrc, R2BayesX, mapdata, maps, maptools, nnet, raster,
spatstat, spdep, zoo, keras, splines2, sdPrior, glogis, glmnet,