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
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pense_2.5.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pense/json (API)

# Install 'pense' in R:

install.packages('pense', repos = c('https://dakep.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:

linear-regression pense regression robust-regresssion robust-statistics openblas cpp openmp

6.63 score 5 stars 71 scripts 441 downloads 29 exports 5 dependencies

Last updated from:b83264d0c1. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	309
linux-devel-x86_64	OK	290
source / vignettes	OK	454
linux-release-arm64	OK	313
linux-release-x86_64	OK	337
macos-release-arm64	OK	217
macos-release-x86_64	OK	402
macos-oldrel-arm64	OK	298
macos-oldrel-x86_64	OK	521
windows-devel	OK	350
windows-release	OK	337
windows-oldrel	OK	336
wasm-release	OK	261

Exports:adamest_cv adapense_cv as_starting_point cd_algorithm_options change_cv_measure consistency_const efficiency_const elnet elnet_cv en_admm_options en_cd_options en_dal_options en_lars_options enpy_initial_estimates enpy_options mloc mlocscale mm_algorithm_options mscale mscale_algorithm_options pense pense_cv prediction_performance prinsens regmest regmest_cv rho_function starting_point tau_size

Dependencies:lattice Matrix Rcpp RcppArmadillo rlang

Controlling the grid of penalization levels

Adjusting the grid of penalization levels | Manually supplying a grid of penalization levels

Rendered fromlambda_grids.Rmdusingknitr::rmarkdown

Last update: 2026-01-14
Started: 2020-09-19

Estimating predictive models

Rendered fromcomputing_adapense.Rmdusingknitr::rmarkdown

Last update: 2026-01-14
Started: 2020-09-19

Citation

Development and contributors

Readme and manuals

Help Manual

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