SVEMnet: Self-Validated Ensemble Models with Lasso and Relaxed Elastic Net Regression

Tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via 'glmnet' (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). Package code was drafted with assistance from generative AI tools.

Version:	3.1.4
Depends:	R (≥ 4.0.0)
Imports:	glmnet (≥ 4.1-6), stats, cluster, ggplot2, lhs, foreach, doParallel, doRNG, parallel, gamlss, gamlss.dist
Suggests:	covr, knitr, rmarkdown, testthat (≥ 3.0.0), withr, vdiffr, RhpcBLASctl
Published:	2025-11-28
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:

Reference manual:

SVEMnet.html , SVEMnet.pdf

Downloads:

Package source:	SVEMnet_3.1.4.tar.gz
Windows binaries:	r-devel: SVEMnet_3.1.4.zip, r-release: SVEMnet_3.1.4.zip, r-oldrel: SVEMnet_3.1.4.zip
macOS binaries:	r-release (arm64): SVEMnet_3.1.4.tgz, r-oldrel (arm64): SVEMnet_3.1.4.tgz, r-release (x86_64): SVEMnet_3.1.4.tgz, r-oldrel (x86_64): SVEMnet_3.1.4.tgz
Old sources:	SVEMnet archive

Linking:

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