R package 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 (2017) <doi:10.1080/10618600.2017.1407325>.
Keywords for this software
References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Emma C. Martin, Alessandro Gasparini, Michael J. Crowther: merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models (2020) arXiv
- Groll, Andreas; Hambuckers, Julien; Kneib, Thomas; Umlauf, Nikolaus: LASSO-type penalization in the framework of generalized additive models for location, scale and shape (2019)
- Kneib, Thomas; Klein, Nadja; Lang, Stefan; Umlauf, Nikolaus: Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions (2019)
- Mathieu Fauvernier; Laurent Remontet; Zoé Uhry; Nadine Bossard; Laurent Roche: survPen: an R package for hazard and excess hazard modelling with multidimensional penalized splines (2019) not zbMATH
- Köhler, Meike; Umlauf, Nikolaus; Beyerlein, Andreas; Winkler, Christiane; Ziegler, Anette-Gabriele; Greven, Sonja: Flexible Bayesian additive joint models with an application to type 1 diabetes research (2017)
- Klein, Nadja; Kneib, Thomas; Lang, Stefan; Sohn, Alexander: Bayesian structured additive distributional regression with an application to regional income inequality in Germany (2015)