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:	1.1.2
Depends:	R (≥ 3.3), mcclust
Imports:	Matrix, MASS, bayesm, cluster, GreedyEPL, gridExtra, ggplot2, methods, utils, stats
Published:	2019-10-24
Author:	Daniela Pauger [aut], Magdalena Leitner [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut], Nicholas G. Polson [ctb], James G. Scott [ctb], Jesse Windle [ctb], Bettina Grün [ctb]
Maintainer:	Magdalena Leitner <effectfusion.jku at gmail.com>
License:	GPL-3
NeedsCompilation:	yes
CRAN checks:	effectFusion results

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