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


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Documentation for package ‘SVEMnet’ version 2.2.4

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SVEMnet-package SVEMnet: Self-Validated Ensemble Models with Relaxed Lasso and Elastic-Net Regression
bigexp_formula Construct a formula for a new response using a bigexp_spec
bigexp_model_matrix Build a model matrix using the spec's stored contrasts (if present)
bigexp_prepare Prepare data to match a bigexp_spec (stable expansion across datasets)
bigexp_terms Create a deterministic expansion spec for wide polynomial/interaction models
bigexp_train Build a spec and prepare training data in one call
coef.svem_model Coefficient Nonzero Percentages from an SVEM Model
glmnet_with_cv Fit a glmnet Model with Cross-Validation
lipid_screen Lipid formulation screening data
plot.svem_model Plot Method for SVEM Models
plot.svem_significance_test Plot SVEM Significance Test Results for Multiple Responses
plot_svem_significance_tests Plot SVEM Significance Test Results for Multiple Responses
predict.svem_cv Predict for svem_cv objects (and convenience wrapper)
predict.svem_model Predict Method for SVEM Models
predict_cv Predict for svem_cv objects (and convenience wrapper)
predict_with_ci Percentile Confidence Intervals for SVEM Predictions
print.svem_significance_test Print Method for SVEM Significance Test
SVEMnet Fit an SVEMnet Model (with optional relaxed elastic net)
svem_optimize_random Random-Search Optimizer with Goals, Weights, Optional CIs, and Diverse Candidates
svem_random_table_multi Generate a Random Prediction Table from Multiple SVEMnet Models (no refit)
svem_significance_test SVEM Significance Test with Mixture Support
svem_significance_test_parallel SVEM Significance Test with Mixture Support (Parallel Version)
with_bigexp_contrasts Evaluate an expression with the spec's recorded contrast options