Package: spikeSlabGAM 1.1-20

spikeSlabGAM: Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models

Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.

Authors:Fabian Scheipl [aut, cre], Bettina Gruen [ctb]

spikeSlabGAM_1.1-20.tar.gz
spikeSlabGAM_1.1-20.zip(r-4.5)spikeSlabGAM_1.1-20.zip(r-4.4)spikeSlabGAM_1.1-20.zip(r-4.3)
spikeSlabGAM_1.1-20.tgz(r-4.4-x86_64)spikeSlabGAM_1.1-20.tgz(r-4.4-arm64)spikeSlabGAM_1.1-20.tgz(r-4.3-x86_64)spikeSlabGAM_1.1-20.tgz(r-4.3-arm64)
spikeSlabGAM_1.1-20.tar.gz(r-4.5-noble)spikeSlabGAM_1.1-20.tar.gz(r-4.4-noble)
spikeSlabGAM_1.1-20.tgz(r-4.4-emscripten)spikeSlabGAM_1.1-20.tgz(r-4.3-emscripten)
spikeSlabGAM.pdf |spikeSlabGAM.html✨
spikeSlabGAM/json (API)
NEWS

# Install 'spikeSlabGAM' in R:

install.packages('spikeSlabGAM', repos = c('https://fabian-s.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fabian-s/spikeslabgam/issues

Uses libs:

openblas– Optimized BLAS

On CRAN:

6.25 score 14 stars 1 packages 14 scripts 900 downloads 3 mentions 14 exports 46 dependencies

Last updated 30 days agofrom:a4e41f293d. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 21 2024
R-4.5-win-x86_64	OK	Nov 21 2024
R-4.5-linux-x86_64	OK	Nov 21 2024
R-4.4-win-x86_64	OK	Nov 21 2024
R-4.4-mac-x86_64	OK	Nov 21 2024
R-4.4-mac-aarch64	OK	Nov 21 2024
R-4.3-win-x86_64	OK	Nov 21 2024
R-4.3-mac-x86_64	OK	Nov 21 2024
R-4.3-mac-aarch64	OK	Nov 21 2024

Exports:evalTerm fct getPosteriorTerm lin mrf plotTerm rnd sm spikeAndSlab spikeSlabGAM srf ssGAM2Bugs ssGAMDesign u

Dependencies:boot cli cluster coda colorspace deldir fansi farver ggplot2 glue gridExtra gtable interp isoband labeling lattice lifecycle magrittr MASS Matrix MatrixModels mcmc MCMCpack mgcv munsell mvtnorm nlme pillar pkgconfig plyr quantreg R2WinBUGS R6 RColorBrewer Rcpp RcppEigen reshape rlang scales SparseM survival tibble utf8 vctrs viridisLite withr

spikeSlabGAM: Bayesian Variable Selection, Model Choice and Regularization for Generalized Additive Mixed Models in R

Rendered fromUsingSpikeSlabGAM-wrapper.Rnwusingutils::Sweaveon Nov 21 2024.

Last update: 2022-05-27
Started: 2022-05-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Get summaries of the posterior (predictive) distribution of the linear predictor of a model term	evalTerm
Generate design for a factor covariate	fct
Get the posterior distribution of the linear predictor of a model term	getPosteriorTerm
Generate orthogonal polynomial base for a numeric covariate without intercept	lin
Generate design for a 2-D Gaussian Markov Random Field	mrf
Generates graphical summaries of a fitted model	plot.spikeSlabGAM
Plot the estimated effect of a model term.	plotTerm
Obtain posterior predictive/credible intervals from a spike-and-slab model	predict.spikeSlabGAM
Print summary for posterior of a 'spikeSlabGAM' fit	print.summary.spikeSlabGAM
Generate design for a random intercept	rnd
Generate a reparameterized P-spline base	sm
Set up and sample a spike-and-slab prior model.	spikeAndSlab
Generate posterior samples for a GAMM with spike-and-slab priors	spikeSlabGAM
Generate design for penalized surface estimation.	srf
Convert samples from a model fitted with 'spikeSlabGAM' into a 'bugs'-object	ssGAM2Bugs
Generate design and model information for 'spikeSlabGAM'	ssGAMDesign
Summary for posterior of a 'spikeSlabGAM' fit	summary.spikeSlabGAM
Generate design for an always included covariate	u