bamlss: 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) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.

Package details

AuthorNikolaus Umlauf [aut, cre] (<>), Nadja Klein [aut] (<>), Achim Zeileis [aut] (<>), Meike Koehler [ctb], Thorsten Simon [aut] (<>), Stanislaus Stadlmann [ctb], Alexander Volkmann [ctb] (<>)
MaintainerNikolaus Umlauf <>
LicenseGPL-2 | GPL-3
Package repositoryView on CRAN
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bamlss documentation built on Nov. 2, 2023, 5:31 p.m.