effectFusion: Bayesian Effect Fusion for Categorical Predictors

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 Polya-Gamma random variables in the case of logistic regression. The code for data augmentation is taken from Polson et al. (2013), who own the copyright.

Version: 1.1.1
Depends: R (≥ 3.3), mcclust
Imports: Matrix, MASS, bayesm, cluster, GreedyEPL, gridExtra, ggplot2, methods, utils, stats
Published: 2019-01-19
Author: Daniela Pauger [aut], Magdalena Leitner [aut, cre], Helga Wagner ORCID iD [aut], Gertraud Malsiner-Walli ORCID iD [aut], Nicholas G. Polson [ctb], James G. Scott [ctb], Jesse Windle [ctb], Bettina GrĂ¼n ORCID iD [ctb]
Maintainer: Magdalena Leitner <magdalena.leitner at jku.at>
License: GPL-3
NeedsCompilation: yes
CRAN checks: effectFusion results

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Reference manual: effectFusion.pdf
Package source: effectFusion_1.1.1.tar.gz
Windows binaries: r-devel: effectFusion_1.1.zip, r-release: effectFusion_1.1.zip, r-oldrel: effectFusion_1.1.1.zip
OS X binaries: r-release: effectFusion_1.1.1.tgz, r-oldrel: effectFusion_1.0.tgz
Old sources: effectFusion archive

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