Package: SVEMnet
Type: Package
Title: Self-Validated Ensemble Models with Lasso and Relaxed Elastic
        Net Regression
Version: 3.2.0
Date: 2026-01-21
Authors@R: 
    person(given = "Andrew T.",
           family = "Karl",
           email = "akarl@asu.edu",
           role = c("cre", "aut"),
           comment = c(ORCID = "0000-0002-5933-8706"))
Description: 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.
URL: https://arxiv.org/abs/2511.20968
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
License: GPL-2 | GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.3
Config/testthat/edition: 3
LazyData: true
NeedsCompilation: no
Packaged: 2026-01-22 20:20:02 UTC; andre
Author: Andrew T. Karl [cre, aut] (ORCID:
    <https://orcid.org/0000-0002-5933-8706>)
Maintainer: Andrew T. Karl <akarl@asu.edu>
Repository: CRAN
Date/Publication: 2026-01-23 09:40:28 UTC
Built: R 4.6.0; ; 2026-04-26 10:43:27 UTC; unix
