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| coef.elnetfit | Extract Model Coefficients |
| coef.pense | Extract Model Coefficients |
| elnet | Elastic Net Estimator for Regression |
| elnet_cv | Cross-validate Elastic Net |
| enpy | PY (Pena-Yohai) initial estimates for EN S-estimators |
| en_options | Additional Options for the EN Algorithms |
| en_options_aug_lars | Additional Options for the EN Algorithms |
| en_options_dal | Additional Options for the EN Algorithms |
| initest_options | Additional Options for the Initial Estimator |
| mscale | Robust M-estimate of Scale |
| mstep_options | Additional Options for the Penalized EN MM-estimator |
| pense | Penalized Elastic Net S-estimators for Regression |
| pensem | Perform an M-step after the EN S-Estimator |
| pensem.default | Perform an M-step after the EN S-Estimator |
| pensem.pense | Perform an M-step after the EN S-Estimator |
| pense_options | Additional Options for the Penalized EN S-estimator |
| plot.cv_elnetfit | Plot Method for Cross-Validated Elastic Net Models |
| plot.elnetfit | Plot Method for Fitted Elastic Net Models |
| plot.pense | Plot Method for Fitted Penalized Elastic Net S/MM-Estimates of Regression |
| predict.elnetfit | Predict Method for the classical Elastic Net Estimator |
| predict.pense | Predict Method for Penalized Elastic Net S- and MM-estimators |
| prinsens | Principal Sensitivity Components |
| residuals.elnetfit | Extract Residuals from a Fitted Elastic-Net Estimator |
| residuals.pense | Extract Residuals from a Fitted Penalized Elastic-Net S/MM-estimator |