effectFusion: Bayesian Effect Fusion for Categorical Predictors

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.

Install the latest version of this package by entering the following in R:
install.packages("effectFusion")
AuthorDaniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]
Date of publication2016-11-29 12:43:49
MaintainerDaniela Pauger <daniela.pauger@jku.at>
LicenseGPL-3
Version1.0

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