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) <doi:10.1080/01621459.2013.829001>) based on latent PolyaGamma random variables in the case of logistic regression. The code for data augmentation is taken from Polson et al. (2013) <doi:10.1080/01621459.2013.829001>, 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 <effectfusion.jku@gmail.com> 
License  GPL3 
Version  1.1.3 
Package repository  View on CRAN 
Installation 
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