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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 |
| 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:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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