gamselBayes: Bayesian Generalized Additive Model Selection

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2023) <doi:10.48550/arXiv.2201.00412>.

Version: 2.0-1
Depends: R (≥ 3.5.0)
Imports: Rcpp, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: Ecdat
Published: 2023-09-03
DOI: 10.32614/CRAN.package.gamselBayes
Author: Virginia X. He ORCID iD [aut], Matt P. Wand ORCID iD [aut, cre]
Maintainer: Matt P. Wand <matt.wand at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: gamselBayes results


Reference manual: gamselBayes.pdf
Vignettes: gamselBayes User Manual


Package source: gamselBayes_2.0-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): gamselBayes_2.0-1.tgz, r-oldrel (arm64): gamselBayes_2.0-1.tgz, r-release (x86_64): gamselBayes_2.0-1.tgz, r-oldrel (x86_64): gamselBayes_2.0-1.tgz
Old sources: gamselBayes archive


Please use the canonical form to link to this page.