R package sdPrior: Scale-Dependent Hyperpriors in Structured Additive Distributional Regression. Utility functions for scale-dependent and alternative hyperpriors. 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. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- 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)
- Ventrucci, Massimo; Rue, Håvard: Penalized complexity priors for degrees of freedom in Bayesian P-splines (2016)