SVEMnet: Self-Validated Ensemble Models with Elastic Net Regression
Implements Self-Validated Ensemble Models (SVEM, Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) using Elastic Net regression via 'glmnet' (Friedman et al. <doi:10.18637/jss.v033.i01>). SVEM averages predictions from multiple models fitted to fractionally weighted bootstraps of the data, tuned with anti-correlated validation weights. Also implements the randomized permutation whole model test for SVEM (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). \\Code for the whole model test was taken from the supplementary material of Karl (2024). Development of this package was assisted by 'GPT o1-preview' for code structure and documentation.
| Version: |
1.3.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
glmnet, stats, gamlss, gamlss.dist, ggplot2, lhs, doParallel, parallel, foreach |
| Suggests: |
knitr, rmarkdown |
| Published: |
2024-12-21 |
| DOI: |
10.32614/CRAN.package.SVEMnet |
| Author: |
Andrew T. Karl 
[cre, aut] |
| Maintainer: |
Andrew T. Karl <akarl at asu.edu> |
| License: |
GPL-2 | GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
SVEMnet citation info |
| Materials: |
NEWS |
| CRAN checks: |
SVEMnet results |
Documentation:
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