bamlss (1.1-1)

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Bayesian Additive Models for Location, Scale, and Shape (and Beyond).

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) and the R package in Umlauf, Klein, Simon, Zeileis (2019) .

Maintainer: Nikolaus Umlauf
Author(s): Nikolaus Umlauf [aut, cre] (<>), Nadja Klein [aut], Achim Zeileis [aut] (<>), Meike Koehler [ctb], Thorsten Simon [aut] (<>), Stanislaus Stadlmann [ctb]

License: GPL-2 | GPL-3

Uses: coda, colorspace, Formula, Matrix, MBA, mgcv, mvtnorm, sp, survival, akima, ff, fields, gamlss, geoR, mapdata, maps, maptools, spatstat, spdep, zoo, glmnet, rjags, BayesX, bit, MASS, nnet, raster, ffbase, glogis, knitr, BayesXsrc, R2BayesX, sdPrior, scoringRules, splines2, keras

Released 2 months ago.

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