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

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Documentation for package ‘SVEMnet’ version 2.5.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

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
bigexp_prepare	Prepare data to match a 'bigexp_spec'
bigexp_terms	Create a deterministic expansion spec for wide polynomial and interaction models
bigexp_train	Build a spec and prepare training data in one call
coef.svem_model	Coefficients for SVEM Models
glmnet_with_cv	Fit a glmnet Model with Repeated Cross-Validation
lipid_screen	Lipid formulation screening data
plot.svem_binomial	Plot Method for SVEM Binomial Models
plot.svem_model	Plot Method for SVEM Models (Gaussian / Generic)
plot.svem_significance_test	Plot SVEM significance test results for one or more responses
predict.svem_cv	Predict for 'svem_cv' objects (and convenience wrapper)
predict.svem_model	Predict Method for SVEM Models (Gaussian and Binomial)
predict_cv	Predict for 'svem_cv' objects (and convenience wrapper)
print.bigexp_spec	Print method for bigexp_spec objects
print.svem_significance_test	Print Method for SVEM Significance Test
SVEMnet	Fit an SVEMnet Model (with optional relaxed elastic net)
svem_nonzero	Coefficient Nonzero Percentages (SVEM)
svem_optimize_random	Random-search optimizer with desirabilities, WMT reweighting, CIs, optimal + exploration candidates, and scored originals
svem_random_table_multi	Generate a Random Prediction Table from Multiple SVEMnet Models (no refit)
svem_significance_test_parallel	SVEM Significance Test with Mixture Support (Parallel Version)
with_bigexp_contrasts	Evaluate code with the spec's recorded contrast options