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) . Self-validated ensemble models (SVEM; Lemkus et al. (2021) ) are fitted for small-sample design-of-experiments and related workflows using 'glmnet' (Friedman et al. (2010) ). 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) ). Package code was drafted with assistance from generative AI tools.