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)
<doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2019)
<arXiv:1909.11784>.
| Version: |
1.1-2 |
| Depends: |
R (≥ 3.5.0), coda, colorspace, mgcv |
| Imports: |
Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel, raster |
| Suggests: |
akima, ff, ffbase, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, R2BayesX, mapdata, maps, maptools, nnet, spatstat, spdep, zoo, keras, splines2, sdPrior, statmod, glogis, glmnet, scoringRules, knitr, MASS |
| Published: |
2020-02-19 |
| Author: |
Nikolaus Umlauf 
[aut, cre],
Nadja Klein [aut],
Achim Zeileis
[aut],
Meike Koehler [ctb],
Thorsten Simon
[aut],
Stanislaus Stadlmann [ctb] |
| Maintainer: |
Nikolaus Umlauf <Nikolaus.Umlauf at uibk.ac.at> |
| License: |
GPL-2 | GPL-3 |
| URL: |
http://www.bamlss.org/ |
| NeedsCompilation: |
yes |
| Citation: |
bamlss citation info |
| Materials: |
NEWS |
| In views: |
Bayesian |
| CRAN checks: |
bamlss results |