Package: pense 2.5.2

David Kepplinger

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

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <doi:10.1214/19-AOAS1269>. The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) <doi:10.1080/00401706.2025.2540970>.

Authors:David Kepplinger [aut, cre], Matías Salibián-Barrera [aut], Gabriela Cohen Freue [aut]

pense_2.5.2.tar.gz
pense_2.5.2.tar.gz(r-4.7-arm64)pense_2.5.2.tar.gz(r-4.7-x86_64)pense_2.5.2.tar.gz(r-4.6-arm64)pense_2.5.2.tar.gz(r-4.6-x86_64)
pense_2.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
pense/json (API)
NEWS

# Install 'pense' in R:
install.packages('pense', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dakep/pense-rpkg/issues

Pkgdown/docs site:https://dakep.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.45 score 71 scripts 233 downloads 29 exports 27 dependencies

Last updated from:302f1a4dea. Checks:4 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE309
linux-devel-x86_64NOTE290
source / vignettesOK479
linux-release-arm64NOTE289
linux-release-x86_64NOTE289
wasm-releaseOK217

Exports:adamest_cvadapense_cvas_starting_pointcd_algorithm_optionschange_cv_measureconsistency_constefficiency_constelnetelnet_cven_admm_optionsen_cd_optionsen_dal_optionsen_lars_optionsenpy_initial_estimatesenpy_optionsmlocmlocscalemm_algorithm_optionsmscalemscale_algorithm_optionspensepense_cvprediction_performanceprinsensregmestregmest_cvrho_functionstarting_pointtau_size

Dependencies:briocallrclicrayondescdiffobjevaluatefsgluejsonlitelatticelifecyclemagrittrMatrixpkgbuildpkgloadpraiseprocessxpsR6RcppRcppArmadillorlangrprojroottestthatwaldowithr

Controlling the grid of penalization levels

David Kepplinger

Rendered fromlambda_grids.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-01-27
Started: 2020-10-05

Estimating predictive models

David Kepplinger

Rendered fromcomputing_adapense.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-01-27
Started: 2020-10-05

Readme and manuals

Help Manual

Help pageTopics
Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimatescd_algorithm_options
Change the Cross-Validation Measurechange_cv_measure
Extract Coefficient Estimatescoef.pense_cvfit
Extract Coefficient Estimatescoef.pense_fit
Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Locationconsistency_const efficiency_const
Compute the Least Squares (Adaptive) Elastic Net Regularization Pathadaelnet adaen elnet
Cross-validation for Least-Squares (Adaptive) Elastic Net Estimateselnet_cv
Use the ADMM Elastic Net Algorithmen_admm_options
Control the Algorithm to Compute (Weighted) Least-Squares Elastic Net Estimatesen_algorithm_options
Use Coordinate Descent to Solve Elastic Net Problemsen_cd_options
Use the DAL Elastic Net Algorithmen_dal_options
Use the LARS Elastic Net Algorithmen_lars_options
ENPY Initial Estimates for EN S-Estimatorsenpy_initial_estimates
Options for the ENPY Algorithmenpy_options
Compute the M-estimate of Locationmloc
Compute the M-estimate of Location and Scalemlocscale
MM-Algorithm to Compute Penalized Elastic Net S- and M-Estimatesmm_algorithm_options
Compute the M-Scale of Centered Valuesmscale
Options for the M-scale Estimation Algorithmmscale_algorithm_options
Compute (Adaptive) Elastic Net S-Estimates of Regressionadapense pense
Cross-validation for (Adaptive) PENSE Estimatesadapense_cv pense_cv
Plot Method for Penalized Estimates With Cross-Validationplot.pense_cvfit
Plot Method for Penalized Estimatesplot.pense_fit
Predict Method for PENSE Fitspredict.pense_cvfit
Predict Method for PENSE Fitspredict.pense_fit
Prediction Performance of Adaptive PENSE Fitsprediction_performance print.pense_pred_perf
Principal Sensitivity Componentsprinsens
Compute (Adaptive) Elastic Net M-Estimates of Regressionregmest
Cross-validation for (Adaptive) Elastic Net M-Estimatesadamest_cv regmest_cv
Extract Residualsresiduals.pense_cvfit
Extract Residualsresiduals.pense_fit
List Available Rho Functionsrho_function
Create Starting Points for the PENSE Algorithmas_starting_point as_starting_point.enpy_starting_points as_starting_point.pense_cvfit as_starting_point.pense_fit starting_point
Summarize Cross-Validated PENSE Fitprint.pense_cvfit summary.pense_cvfit
Compute the Tau-Scale of Centered Valuestau_size