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:	1.0.3
Depends:	R (≥ 3.5.0)
Imports:	glmnet, stats, gamlss, gamlss.dist, ggplot2, lhs
Published:	2024-11-20
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
CRAN checks:	SVEMnet results

Documentation:

Reference manual:

SVEMnet.pdf

Downloads:

Package source:	SVEMnet_1.0.3.tar.gz
Windows binaries:	r-devel: SVEMnet_1.0.3.zip, r-release: SVEMnet_1.0.3.zip, r-oldrel: SVEMnet_1.0.3.zip
macOS binaries:	r-release (arm64): SVEMnet_1.0.3.tgz, r-oldrel (arm64): SVEMnet_1.0.3.tgz, r-release (x86_64): SVEMnet_1.0.3.tgz, r-oldrel (x86_64): SVEMnet_1.0.3.tgz

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