Package: SVEMnet 3.6.2

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

Implements the self-validated elastic-net and relaxed elastic-net ensemble modeling and multi-response optimization workflow described in Karl (2026) <doi:10.1016/j.chemolab.2026.105660>. Self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) are fitted for small-sample design-of-experiments and related workflows using '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.

Authors:Andrew T. Karl [cre, aut]