# Penalized Elastic Net S/MM-Estimator of Regression

## Documentation for package ‘pense’ version 1.2.9

- DESCRIPTION file.

## Help Pages

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