SVEMnet: Self-Validated Ensemble Models with Lasso and Relaxed ElasticNet 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 |
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