Reference

Robust fitting of linear regression models
Fitting and estimating prediction performance Fit linear regression models for a set of penalization levels and estimate the prediction performance via cross-validation.
pense_cv() adapense_cv()	Cross-validation for (Adaptive) PENSE Estimates
pensem_cv()	Compute Penalized Elastic Net M-Estimates from PENSE
regmest_cv() adamest_cv()	Cross-validation for (Adaptive) Elastic Net M-Estimates
Fitting only
pense()	Compute (Adaptive) Elastic Net S-Estimates of Regression
regmest()	Compute (Adaptive) Elastic Net M-Estimates of Regression
Plotting and printing Methods for plotting and summarizing fits.
plot(<pense_cvfit>)	Plot Method for Penalized Estimates With Cross-Validation
plot(<pense_fit>)	Plot Method for Penalized Estimates
prediction_performance() print(<pense_pred_perf>)	Prediction Performance of Adaptive PENSE Fits
summary(<pense_cvfit>) print(<pense_cvfit>)	Summarize Cross-Validated PENSE Fit
Extracting information Methods for extracting coefficient estimates and predicting values.
coef(<pense_cvfit>)	Extract Coefficient Estimates
coef(<pense_fit>)	Extract Coefficient Estimates
predict(<pense_cvfit>)	Predict Method for PENSE Fits
predict(<pense_fit>)	Predict Method for PENSE Fits
residuals(<pense_cvfit>)	Extract Residuals
residuals(<pense_fit>)	Extract Residuals
Robust location and scale Compute robust location and scale estimates.
mloc()	Compute the M-estimate of Location
mlocscale()	Compute the M-estimate of Location and Scale
mscale()	Compute the M-Scale of Centered Values
tau_size()	Compute the Tau-Scale of Centered Values
Non-robust methods Non-robust methods for fitting linear regression models.
elnet()	Compute the Least Squares (Adaptive) Elastic Net Regularization Path
elnet_cv()	Cross-validation for Least-Squares (Adaptive) Elastic Net Estimates
Advanced functionality
Control options
cd_algorithm_options()	Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimates
en_options_aug_lars() en_options_dal()	Deprecated
en_admm_options()	Use the ADMM Elastic Net Algorithm
en_algorithm_options	Control the Algorithm to Compute (Weighted) Least-Squares Elastic Net Estimates
en_cd_options()	Use Coordinate Descent to Solve Elastic Net Problems
en_dal_options()	Use the DAL Elastic Net Algorithm
en_lars_options()	Use the LARS Elastic Net Algorithm
enpy_options()	Options for the ENPY Algorithm
initest_options()	Deprecated
mm_algorithm_options()	MM-Algorithm to Compute Penalized Elastic Net S- and M-Estimates
mscale_algorithm_options()	Options for the M-scale Estimation Algorithm
mstep_options()	Deprecated
pense_options()	Deprecated
Initial estimates Manually compute and alter initial estimates.
enpy_initial_estimates()	ENPY Initial Estimates for EN S-Estimators
prinsens()	Principal Sensitivity Components
starting_point() as_starting_point()	Create Starting Points for the PENSE Algorithm
Miscellaneous
consistency_const()	Get the Constant for Consistency for the M-Scale
rho_function()	List Available Rho Functions
Deprecated functions
en_options_aug_lars() en_options_dal()	Deprecated
enpy()	Deprecated
initest_options()	Deprecated
mstep_options()	Deprecated
pense_options()	Deprecated
pensem()	Deprecated Alias of pensem_cv