Self-Validated Ensemble Models with Lasso and Relaxed Elastic Net Regression
Construct a formula for a new response using a bigexp_spec
Prepare data to match a bigexp_spec
Create a deterministic expansion spec for wide polynomial and interact...
Build a spec and prepare training data in one call
Coefficients for SVEM Models
Fit a glmnet Model with Repeated Cross-Validation
Plot Method for SVEM Binomial Models
Plot Method for SVEM Models (Gaussian / Generic)
Plot SVEM significance test results for one or more responses
Predict from glmnet_with_cv Fits (svem_cv Objects)
Predict Method for SVEM Models (Gaussian and Binomial)
Print method for bigexp_spec objects
Print Method for SVEM Significance Test
Export SVEM candidate sets to CSV
Coefficient Nonzero Percentages (SVEM)
Generate a Random Prediction Table from Multiple SVEMnet Models (no re...
Random-search scoring for SVEM models
Select best row and diverse candidates from an SVEM score table
SVEM whole-model significance test with mixture support (parallel)
Whole-model tests for multiple SVEM responses (WMT wrapper)
SVEMnet: Self-Validated Ensemble Models with Relaxed Lasso and Elastic...
Fit an SVEMnet model (Self-Validated Ensemble Elastic Net)
Evaluate code with the spec's recorded contrast options
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.