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) <http://EconPapers.RePEc.org/RePEc:inn:wpaper:2017-05>.
Version: | 0.1-2 |
Depends: | R (≥ 3.2.3), coda, colorspace, mgcv |
Imports: | Formula, MBA, mvtnorm, sp, spam, Matrix, survival, methods, parallel |
Suggests: | akima, bit, fields, gamlss, geoR, rjags, BayesX, BayesXsrc, mapdata, maps, maptools, raster, spatstat, spdep, zoo |
Published: | 2017-04-14 |
Author: | Nikolaus Umlauf [aut, cre], Nadja Klein [aut], Achim Zeileis [aut], Meike Koehler [aut] |
Maintainer: | Nikolaus Umlauf <Nikolaus.Umlauf at uibk.ac.at> |
License: | GPL-2 | GPL-3 |
NeedsCompilation: | yes |
Citation: | bamlss citation info |
Materials: | ChangeLog |
CRAN checks: | bamlss results |
Reference manual: | bamlss.pdf |
Package source: | bamlss_0.1-2.tar.gz |
Windows binaries: | r-devel: bamlss_0.1-2.zip, r-release: bamlss_0.1-2.zip, r-oldrel: bamlss_0.1-2.zip |
OS X El Capitan binaries: | r-release: bamlss_0.1-2.tgz |
OS X Mavericks binaries: | r-oldrel: bamlss_0.1-2.tgz |
Old sources: | bamlss archive |
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