Package: effectFusion
Title: Bayesian Effect Fusion for Categorical Predictors
Version: 1.0
Date: 2016-11-21
Author: Daniela Pauger [aut, cre], Helga Wagner [aut], Gertraud Malsiner-Walli [aut]
Maintainer: Daniela Pauger <daniela.pauger@jku.at>
Description: 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.
Depends: R (>= 3.3.1)
License: GPL-3
Imports: Matrix, MASS, bayesm, cluster, ggplot2, utils, stats
Encoding: UTF-8
LazyData: true
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-11-29 08:46:14 UTC; AK115491
Repository: CRAN
Date/Publication: 2016-11-29 12:43:49
Built: R 3.3.3; ; 2018-04-22 17:20:15 UTC; windows
