Skip to contents

pense R package

R CMD check

This R package implements the penalized adaptive elastic net S-estimator (adaptive PENSE), the non-adaptive version (PENSE) and the penalized M-step (PENSEM) as proposed in Cohen Freue, et al. (2019) and Kepplinger (2023). Hyper-parameter selection is done by robust information sharing cross-validation (RIS-CV; Kepplinger & Wei 2025).

Usage

The main function for most users is adapense_cv(), which computes an adaptive PENSE fit and estimates prediction performance for many values of the penalization level.

library(pense)

# Generate dummy data with n=50 observations and p=25 possible predictors
# (of which only the first 3 are truly relevant).
n <- 50
p <- 25
set.seed(123)
x <- matrix(rt(n * p, df = 5), ncol = p)
y <- x[, 1] + 0.5 * x[, 2] + 2 * x[, 3] + rt(n, df = 2)

# Compute an adaptive PENSE fit with hyper-parameters alpha=0.8, exponent=2,
# and 50 different values for the penalization level.
# Prediction performance of the 50 fits for different penalization levels
# is estimated with 5-fold cross-validation,
# repeated 10 times (the more the better!).
# On selected platforms use 2 CPU cores.
set.seed(123) # Setting the seed is suggested for reproducibility of the CV results.
fit <- adapense_cv(x, y, alpha = 0.9, cv_k = 5, cv_repl = 10, ncores = 2)

# Visualize the estimated prediction performance using plot()
plot(fit)

# Summarize the model with best prediction performance using summary()
summary(fit)

# Summarize the model with "almost as-good prediction performance" as the best
# model using summary(lambda = "se")
summary(fit, lambda = "se")

The user can also compute a non-adaptive PENSE fit by using pense_cv() in place of adapense_cv() or a PENSEM fit by using pensem_cv().

Detailed examples

The package vignette Estimating predictive models demonstrate in more detail how to compute adaptive and non-adaptive PENSE estimates.

Installation

To install the latest release from CRAN, run the following R code in the R console:

The most recent development version can be installed directly from github using the devtools package:

# Install the most recent development version:
devtools::install_github("dakep/pense-rpkg")

References

  • D. Kepplinger and S. Wei, “Information sharing for robust and stable cross-validation,” Technometrics, 2025, doi: 10.1080/00401706.2025.2540970.
  • D. Kepplinger, “Robust variable selection and estimation via adaptive elastic net S-estimators for linear regression,” Computational Statistics & Data Analysis, vol. 183, 2023, doi: 10.1016/j.csda.2023.107730.
  • G. V. Cohen Freue, D. Kepplinger, M. Salibián-Barrera, and E. Smucler, “Robust elastic net estimators for variable selection and identification of proteomic biomarkers,” Annals of Applied Statistics, vol. 13, no. 4, pp. 2065–2090, 2019, doi: 10.1214/19-AOAS1269.