R: Penalized Elastic Net S/MM-Estimator of Regression

# Penalized Elastic Net S/MM-Estimator of Regression

## Documentation for package ‘pense’ version 2.5.2

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

## Help Pages

adaelnet | Compute the Least Squares (Adaptive) Elastic Net Regularization Path
adaen | Compute the Least Squares (Adaptive) Elastic Net Regularization Path
adamest_cv | Cross-validation for (Adaptive) Elastic Net M-Estimates
adapense | Compute (Adaptive) Elastic Net S-Estimates of Regression
adapense_cv | Cross-validation for (Adaptive) PENSE Estimates
as_starting_point | Create Starting Points for the PENSE Algorithm
as_starting_point.enpy_starting_points | Create Starting Points for the PENSE Algorithm
as_starting_point.pense_cvfit | Create Starting Points for the PENSE Algorithm
as_starting_point.pense_fit | Create Starting Points for the PENSE Algorithm
cd_algorithm_options | Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimates
change_cv_measure | Change the Cross-Validation Measure
coef.pense_cvfit | Extract Coefficient Estimates
coef.pense_fit | Extract Coefficient Estimates
consistency_const | Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Location
efficiency_const | Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Location
elnet | Compute the Least Squares (Adaptive) Elastic Net Regularization Path
elnet_cv | Cross-validation for Least-Squares (Adaptive) Elastic Net Estimates
enpy_initial_estimates | ENPY Initial Estimates for EN S-Estimators
enpy_options | Options for the ENPY Algorithm
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
mloc | Compute the M-estimate of Location
mlocscale | Compute the M-estimate of Location and Scale
mm_algorithm_options | MM-Algorithm to Compute Penalized Elastic Net S- and M-Estimates
mscale | Compute the M-Scale of Centered Values
mscale_algorithm_options | Options for the M-scale Estimation Algorithm
pense | Compute (Adaptive) Elastic Net S-Estimates of Regression
pense_cv | Cross-validation for (Adaptive) PENSE Estimates
plot.pense_cvfit | Plot Method for Penalized Estimates With Cross-Validation
plot.pense_fit | Plot Method for Penalized Estimates
predict.pense_cvfit | Predict Method for PENSE Fits
predict.pense_fit | Predict Method for PENSE Fits
prediction_performance | Prediction Performance of Adaptive PENSE Fits
prinsens | Principal Sensitivity Components
print.pense_cvfit | Summarize Cross-Validated PENSE Fit
print.pense_pred_perf | Prediction Performance of Adaptive PENSE Fits
regmest | Compute (Adaptive) Elastic Net M-Estimates of Regression
regmest_cv | Cross-validation for (Adaptive) Elastic Net M-Estimates
residuals.pense_cvfit | Extract Residuals
residuals.pense_fit | Extract Residuals
rho_function | List Available Rho Functions
starting_point | Create Starting Points for the PENSE Algorithm
summary.pense_cvfit | Summarize Cross-Validated PENSE Fit
tau_size | Compute the Tau-Scale of Centered Values