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 (2021) <arXiv:2201.00412>.

Version: 1.0-2
Depends: R (≥ 3.5.0)
Imports: Rcpp, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: Ecdat
Published: 2022-02-09
Author: Virginia X. He [aut], Matt P. Wand ORCID iD [aut, cre]
Maintainer: Matt P. Wand <matt.wand at uts.edu.au>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: gamselBayes results

Documentation:

Reference manual: gamselBayes.pdf
Vignettes: gamselBayes User Manual

Downloads:

Package source: gamselBayes_1.0-2.tar.gz
Windows binaries: r-devel: gamselBayes_1.0-2.zip, r-release: gamselBayes_1.0-2.zip, r-oldrel: gamselBayes_1.0-2.zip
macOS binaries: r-release (arm64): gamselBayes_1.0-2.tgz, r-release (x86_64): gamselBayes_1.0-2.tgz, r-oldrel: gamselBayes_1.0-2.tgz
Old sources: gamselBayes archive

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