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>.
| Version: |
1.0-1 |
| 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 |
| Published: |
2018-10-12 |
| Author: |
Nikolaus Umlauf [aut, cre],
Nadja Klein [aut],
Achim Zeileis  [aut],
Meike Koehler [aut],
Thorsten Simon [ctb],
Stanislaus Stadlmann [ctb] |
| Maintainer: |
Nikolaus Umlauf <Nikolaus.Umlauf at uibk.ac.at> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
yes |
| Citation: |
bamlss citation info |
| Materials: |
ChangeLog |
| In views: |
Bayesian |
| CRAN checks: |
bamlss results |