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.
- Version1.2-5
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- bamlss citation info
- Last release10/11/2024
Documentation
Team
Nikolaus Umlauf
Achim Zeileis
Alexander Volkmann
Show author detailsRolesContributorStanislaus Stadlmann
Show author detailsRolesContributorNadja Klein
Show author detailsRolesAuthorMeike Koehler
Show author detailsRolesContributorThorsten Simon
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- Depends4 packages
- Imports6 packages
- Suggests25 packages
- Reverse Depends2 packages
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