# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "effectFusion" in publications use:' type: software license: GPL-3.0-only title: 'effectFusion: Bayesian Effect Fusion for Categorical Predictors' version: 1.1.3.9002 doi: 10.32614/CRAN.package.effectFusion abstract: 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. Compatibility updates by Renova. authors: - family-names: Pauger given-names: Daniela - family-names: Leitner given-names: Magdalena email: effectfusion.jku@gmail.com - family-names: Wagner given-names: Helga orcid: https://orcid.org/0000-0002-7003-9512 - family-names: Malsiner-Walli given-names: Gertraud orcid: https://orcid.org/0000-0002-1213-4749 repository: https://renova-r.r-universe.dev commit: e1fdf2ba24c5600fd1c317682787a513017b05ed date-released: '2026-11-13' contact: - family-names: Leitner given-names: Magdalena email: effectfusion.jku@gmail.com