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pense (version 2.5.2)

Penalized Elastic Net S/MM-Estimator of Regression

Description

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) .

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Install

install.packages('pense')

Monthly Downloads

493

Version

2.5.2

License

MIT + file LICENSE

Issues

1

Pull Requests

0

Stars

5

Forks

2

Maintainer

David Kepplinger

Last Published

January 27th, 2026

Functions in pense (2.5.2)

mloc

Compute the M-estimate of Location
mlocscale

Compute the M-estimate of Location and Scale
mscale_derivative

Compute the Gradient and Hessian of the M-Scale Function
mscale_algorithm_options

Options for the M-scale Estimation Algorithm
enpy_options

Options for the ENPY Algorithm
en_lars_options

Use the LARS Elastic Net Algorithm
enpy_initial_estimates

ENPY Initial Estimates for EN S-Estimators
mscale

Compute the M-Scale of Centered Values
en_ridge_options

Ridge optimizer using an Augmented data matrix. Only available for Ridge problems (`alpha=0``) and selected automatically in this case.
mm_algorithm_options

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

Prediction Performance of Adaptive PENSE Fits
prinsens

Principal Sensitivity Components
regmest

Compute (Adaptive) Elastic Net M-Estimates of Regression
print.nsoptim_metrics

Print Metrics
predict.pense_cvfit

Predict Method for PENSE Fits
predict.pense_fit

Predict Method for PENSE Fits
pense

Compute (Adaptive) Elastic Net S-Estimates of Regression
plot.pense_cvfit

Plot Method for Penalized Estimates With Cross-Validation
pense_cv

Cross-validation for (Adaptive) PENSE Estimates
plot.pense_fit

Plot Method for Penalized Estimates
tau_size

Compute the Tau-Scale of Centered Values
starting_point

Create Starting Points for the PENSE Algorithm
residuals.pense_cvfit

Extract Residuals
rho_function

List Available Rho Functions
consistency_const

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

Cross-validation for (Adaptive) Elastic Net M-Estimates
residuals.pense_fit

Extract Residuals
summary.pense_cvfit

Summarize Cross-Validated PENSE Fit
.find_stable_bdb_bisquare

Determine a breakdown point with stable numerical properties of the M-scale with Tukey's bisquare rho function.
coef.pense_cvfit

Extract Coefficient Estimates
change_cv_measure

Change the Cross-Validation Measure
coef.pense_fit

Extract Coefficient Estimates
cd_algorithm_options

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

Compute the Least Squares (Adaptive) Elastic Net Regularization Path
en_cd_options

Use Coordinate Descent to Solve Elastic Net Problems
en_algorithm_options

Control the Algorithm to Compute (Weighted) Least-Squares Elastic Net Estimates
en_dal_options

Use the DAL Elastic Net Algorithm
en_admm_options

Use the ADMM Elastic Net Algorithm
.bisquare_efficiency_const

Get the constant for the desired efficiency of the M-estimate of location using the bisquare \(\rho\) function
.run_replicated_cv_ris

Run replicated K-fold CV with random splits, matching the global estimates to the CV estimates by Kendall's tau-b computed on the robustness weights.
.mopt_efficiency_const

Get the constant for the desired efficiency of the M-estimate of location using the optimal \(\rho\) function
.mopt_consistency_const

Get the Constant for Consistency for the M-Scale Using the Optimal Rho Function
.bisquare_consistency_const

Get the Constant for Consistency for the M-Scale Using the Bisquare Rho Function
.approx_match

Approximate Value Matching
.huber_efficiency_const

Get the constant for the desired efficiency of the M-estimate of location using the Huber \(\rho\) function
.standardize_data

Standardize data
.run_replicated_cv

Run replicated K-fold CV with random splits
elnet_cv

Cross-validation for Least-Squares (Adaptive) Elastic Net Estimates