Variable selection and Bayesian effect fusion for categorical predictors in linear and logistic regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or by taking model averages. Posterior inference is accomplished by an MCMC sampling scheme which makes use of a data augmentation strategy (Polson, Scott & Windle (2013)) based on latent PolyaGamma random variables in the case of logistic regression. The code for data augmentation is taken from Polson et al. (2013), who own the copyright.
Package details 


Author  Daniela Pauger [aut], Magdalena Leitner [aut, cre], Helga Wagner [aut] (<https://orcid.org/0000000270039512>), Gertraud MalsinerWalli [aut] (<https://orcid.org/0000000212134749>), Nicholas G. Polson [ctb], James G. Scott [ctb], Jesse Windle [ctb], Bettina Grün [ctb] (<https://orcid.org/0000000172654773>) 
Maintainer  Magdalena Leitner <[email protected]> 
License  GPL3 
Version  1.1.1 
Package repository  View on CRAN 
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