Package: gamselBayes 2.0-1

Matt P. Wand

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) <arxiv:2201.00412>.

Authors:Virginia X. He [aut], Matt P. Wand [aut, cre]

gamselBayes_2.0-1.tar.gz
gamselBayes_2.0-1.tar.gz(r-4.5-noble)gamselBayes_2.0-1.tar.gz(r-4.4-noble)
gamselBayes_2.0-1.tgz(r-4.4-emscripten)gamselBayes_2.0-1.tgz(r-4.3-emscripten)
gamselBayes.pdf |gamselBayes.html
gamselBayes/json (API)

# Install 'gamselBayes' in R:
install.packages('gamselBayes', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

2.00 score 10 scripts 234 downloads 7 exports 2 dependencies

Last updated 1 years agofrom:84e5342b76. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 28 2024
R-4.5-linux-x86_64OKNov 28 2024

Exports:checkChainseffectTypeseffectTypesVectorgamselBayesgamselBayes.controlgamselBayesUpdategamselBayesVignette

Dependencies:RcppRcppArmadillo

gamselBayes User Manual

Rendered frommanual.Rnwusingutils::Sweaveon Nov 28 2024.

Last update: 2023-04-12
Started: 2022-01-20