Authors@R: c(person("Daniela", "Pauger", role = c("aut")),
person("Magdalena", "Leitner", role = c("aut", "cre"),
person("Gertraud", "Malsiner-Walli", role = "aut", comment = c(ORCID = "0000-0002-1213-4749")),
person("Nicholas G.", "Polson", role = "ctb"),
person("James G.", "Scott", role = "ctb"),
person("Jesse", "Windle", role = "ctb"),
Imports: Matrix, MASS, bayesm, cluster, GreedyEPL, gridExtra, ggplot2,
Description: 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.

(2013) <doi:10.1080/01621459.2013.829001>) 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)

Description: 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.