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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 (2021) <doi:10.18637/jss.v100.i04>.
Version: | 1.2-5 |
Depends: | R (≥ 3.5.0), coda, colorspace, distributions3 (≥ 0.2.1), mgcv |
Imports: | Formula, MBA, mvtnorm, sp, Matrix, survival, methods, parallel |
Suggests: | bit, ff, fields, gamlss, gamlss.dist, interp, rjags, BayesX, mapdata, maps, sf, nnet, spatstat, spdep, zoo, keras, splines2, sdPrior, statmod, glogis, glmnet, scoringRules, knitr, rmarkdown, MASS, tensorflow |
Published: | 2024-10-11 |
DOI: | 10.32614/CRAN.package.bamlss |
Author: | Nikolaus Umlauf [aut, cre], Nadja Klein [aut], Achim Zeileis [aut], Meike Koehler [ctb], Thorsten Simon [aut], Stanislaus Stadlmann [ctb], Alexander Volkmann [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, MixedModels |
CRAN checks: | bamlss results |
Reference manual: | bamlss.pdf |
Vignettes: |
First Steps (source, R code) |
Package source: | bamlss_1.2-5.tar.gz |
Windows binaries: | r-devel: bamlss_1.2-5.zip, r-release: bamlss_1.2-5.zip, r-oldrel: bamlss_1.2-5.zip |
macOS binaries: | r-release (arm64): bamlss_1.2-5.tgz, r-oldrel (arm64): bamlss_1.2-5.tgz, r-release (x86_64): bamlss_1.2-5.tgz, r-oldrel (x86_64): bamlss_1.2-5.tgz |
Old sources: | bamlss archive |
Reverse depends: | gmfamm, MJMbamlss |
Reverse imports: | distreg.vis, mevr, spiky |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.