Package index

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.

change_cv_measure()

Change the Cross-Validation Measure

pense_cv() adapense_cv()

Cross-validation for (Adaptive) PENSE Estimates

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

Controlling the Robust EN algorithm

Options to choose and control the algorithm to optimize robust EN objective functions

cd_algorithm_options()

Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimates

mm_algorithm_options()

MM-Algorithm to Compute Penalized Elastic Net S- and M-Estimates

Controlling the EN algorithm

Options to choose and control the algorithm to optimize least-squares EN problems.

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

Rho/Psi functions

mscale_algorithm_options()

Options for the M-scale Estimation Algorithm

consistency_const() efficiency_const()

Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Location

rho_function()

List Available Rho Functions

Initial estimates

Manually compute and alter initial estimates.

enpy_initial_estimates()

ENPY Initial Estimates for EN S-Estimators

enpy_options()

Options for the ENPY Algorithm

prinsens()

Principal Sensitivity Components

starting_point() as_starting_point()

Create Starting Points for the PENSE Algorithm

Miscellaneous