Package: pense 2.2.2

David Kepplinger

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

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The methods are proposed in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <https://projecteuclid.org/euclid.aoas/1574910036>. The package implements the extensions and algorithms described in Kepplinger, D. (2020) <doi:10.14288/1.0392915>.

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

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pense.pdf |pense.html✨
pense/json (API)
NEWS

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

Peer review:

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

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

On CRAN:

linear-regressionpenseregressionrobust-regresssionrobust-statistics

6.06 score 4 stars 48 scripts 357 downloads 35 exports 8 dependencies

Last updated 4 months agofrom:dfb3bd414d. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-win-x86_64OKNov 23 2024
R-4.5-linux-x86_64OKNov 23 2024
R-4.4-win-x86_64OKNov 23 2024
R-4.4-mac-x86_64OKNov 23 2024
R-4.4-mac-aarch64OKNov 23 2024
R-4.3-win-x86_64OKNov 23 2024
R-4.3-mac-x86_64OKNov 23 2024
R-4.3-mac-aarch64OKNov 23 2024

Exports:adamest_cvadapense_cvas_starting_pointcd_algorithm_optionsconsistency_constelnetelnet_cven_admm_optionsen_cd_optionsen_dal_optionsen_lars_optionsen_options_aug_larsen_options_dalenpyenpy_initial_estimatesenpy_optionsinitest_optionsmlocmlocscalemm_algorithm_optionsmscalemscale_algorithm_optionsmstep_optionspensepense_cvpense_optionspensempensem_cvprediction_performanceprinsensregmestregmest_cvrho_functionstarting_pointtau_size

Dependencies:cligluelatticelifecycleMatrixRcppRcppArmadillorlang

Controlling the grid of penalization levels

David Kepplinger

Rendered fromlambda_grids.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2021-07-05
Started: 2020-09-19

Estimating predictive models

David Kepplinger

Rendered fromcomputing_adapense.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2021-07-05
Started: 2020-09-19

Migrating from pense version 1.x to 2.x

David Kepplinger

Rendered frommigration_guide.Rmdusingknitr::rmarkdownon Nov 23 2024.

Last update: 2020-09-19
Started: 2020-09-16

Readme and manuals

Help Manual

Help pageTopics
Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimatescd_algorithm_options
Extract Coefficient Estimatescoef.pense_cvfit
Extract Coefficient Estimatescoef.pense_fit
Get the Constant for Consistency for the M-Scaleconsistency_const
Deprecateddeprecated_en_options en_options_aug_lars en_options_dal
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
Deprecatedenpy
ENPY Initial Estimates for EN S-Estimatorsenpy_initial_estimates
Options for the ENPY Algorithmenpy_options
Deprecatedinitest_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
Deprecatedmstep_options
Compute (Adaptive) Elastic Net S-Estimates of Regressionadapense pense
Cross-validation for (Adaptive) PENSE Estimatesadapense_cv pense_cv
Deprecatedpense_options
Deprecated Alias of pensem_cvpensem
Compute Penalized Elastic Net M-Estimates from PENSEpensem_cv pensem_cv.default pensem_cv.pense_cvfit
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