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 [aut], Matt P. Wand [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_2.0-1.tar.gz
Windows binaries:	r-devel: gamselBayes_2.0-1.zip, r-release: gamselBayes_2.0-1.zip, r-oldrel: gamselBayes_2.0-1.zip
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

Linking:

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