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 
[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 |
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