Variable selection and Bayesian effect fusion for categorical predictors in linear 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 model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.
Package details 


Author  Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud MalsinerWalli [aut] 
Date of publication  20161129 12:43:49 
Maintainer  Daniela Pauger <daniela.pauger@jku.at> 
License  GPL3 
Version  1.0 
Package repository  View on CRAN 
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