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pense (version 2.2.2)

Penalized Elastic Net S/MM-Estimator of Regression

Description

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) . The package implements the extensions and algorithms described in Kepplinger, D. (2020) .

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Install

install.packages('pense')

Monthly Downloads

254

Version

2.2.2

License

MIT + file LICENSE

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Repository

https://github.com/dakep/pense-rpkg

Homepage

https://dakep.github.io/pense-rpkg/

Maintainer

David Kepplinger

Last Published

July 27th, 2024

Functions in pense (2.2.2)

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

en_lars_options

Use the LARS Elastic Net Algorithm

en_admm_options

Use the ADMM Elastic Net Algorithm

initest_options

mscale_derivative

Compute the Gradient and Hessian of the M-Scale Function

Options for the ENPY Algorithm

mscale_algorithm_options

Options for the M-scale Estimation Algorithm

Use Coordinate Descent to Solve Elastic Net Problems

Compute the M-estimate of Location

Compute the M-Scale of Centered Values

Compute the M-estimate of Location and Scale

mm_algorithm_options

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

enpy_initial_estimates

ENPY Initial Estimates for EN S-Estimators

Deprecated Alias of pensem_cv

plot.pense_cvfit

Plot Method for Penalized Estimates With Cross-Validation

Principal Sensitivity Components

Compute (Adaptive) Elastic Net S-Estimates of Regression

Compute Penalized Elastic Net M-Estimates from PENSE

prediction_performance

Prediction Performance of Adaptive PENSE Fits

residuals.pense_fit

Extract Residuals

summary.pense_cvfit

Summarize Cross-Validated PENSE Fit

Plot Method for Penalized Estimates

Cross-validation for (Adaptive) Elastic Net M-Estimates

Create Starting Points for the PENSE Algorithm

residuals.pense_cvfit

Extract Residuals

print.nsoptim_metrics

Compute (Adaptive) Elastic Net M-Estimates of Regression

Cross-validation for (Adaptive) PENSE Estimates

predict.pense_cvfit

Predict Method for PENSE Fits

List Available Rho Functions

Compute the Tau-Scale of Centered Values

predict.pense_fit

Predict Method for PENSE Fits

cd_algorithm_options

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

coef.pense_cvfit

Extract Coefficient Estimates

deprecated_en_options

.run_replicated_cv

Run replicated K-fold CV with random splits

Extract Coefficient Estimates

consistency_const

Get the Constant for Consistency for the M-Scale

Compute the Least Squares (Adaptive) Elastic Net Regularization Path

Use the DAL Elastic Net Algorithm

.standardize_data

Standardize data

Approximate Value Matching

en_ridge_options

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

.bisquare_consistency_const

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

.find_stable_bdb_bisquare

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

en_algorithm_options

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