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: |
3.2.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glmnet (≥ 4.1-6), stats, cluster, ggplot2, lhs, foreach, doParallel, parallel, gamlss, gamlss.dist, utils |
| Suggests: |
covr, knitr, rmarkdown, testthat (≥ 3.0.0), withr, vdiffr, RhpcBLASctl |
| Published: |
2026-01-23 |
| 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 |
| URL: |
https://arxiv.org/abs/2511.20968 |
| NeedsCompilation: |
no |
| Citation: |
SVEMnet citation info |
| Materials: |
NEWS |
| CRAN checks: |
SVEMnet results |
Documentation:
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