Penalized Elastic Net S/MM-Estimator of Regression

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Documentation for package ‘pense’ version 2.5.0

• 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