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.

Version: 1.0
Depends: R (≥ 3.3.1)
Imports: Matrix, MASS, bayesm, cluster, ggplot2, utils, stats
Published: 2016-11-29
Author: Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]
Maintainer: Daniela Pauger <daniela.pauger at jku.at>
License: GPL-3
NeedsCompilation: no
CRAN checks: effectFusion results


Reference manual: effectFusion.pdf
Package source: effectFusion_1.0.tar.gz
Windows binaries: r-devel: effectFusion_1.0.zip, r-release: effectFusion_1.0.zip, r-oldrel: effectFusion_1.0.zip
OS X El Capitan binaries: r-release: effectFusion_1.0.tgz
OS X Mavericks binaries: r-oldrel: effectFusion_1.0.tgz


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