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gamselBayes (version 2.0-1)

Bayesian Generalized Additive Model Selection

Description

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) .

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Version

Install

install.packages('gamselBayes')

Monthly Downloads

262

Version

2.0-1

License

GPL (>= 2)

Maintainer

Matt Wand

Last Published

September 3rd, 2023

Functions in gamselBayes (2.0-1)

plot.gamselBayes

Plot components of the selected generalized additive model from a gamselBayes() fit
effectTypes

Tabulate the estimated effect types from a Bayesian generalized additive model object
gamselBayesUpdate

Update a gamselBayes() fit object.
gamselBayes.control

Controlling Bayesian generalized additive model selection
checkChains

Check Markov chain Monte Carlo samples
gamselBayesVignette

Display the package's vignette.
predict.gamselBayes

Obtain predictions from a gamselBayes() fit
effectTypesVector

Obtain the estimated effect types from a Bayesian generalized additive model object
gamselBayes

Bayesian generalized additive model selection including a fast variational option
summary.gamselBayes

Summarise components of the selected generalized additive model from a gamselBayes() fit