This R package implements the penalized elastic net S-estimator (PENSE) and the penalized M-step (PENSEM) as proposed in Cohen Freue, et al. (2019) as well as the adaptive extensions developed in Kepplinger (2020).

## Migrating from pense versions 1.x to 2.x

Version 2.x, release September 2020, introduces many new features and improved computational speed. The changes, however, make the package incompatible with previous versions. The migration guide helps migrating existing code to new versions of the package.

## 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:

install.packages("pense")


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

• Kepplinger, D. (2020). Robust estimation and variable selection in high-dimensional linear regression models. University of British Columbia. available online.
• Cohen Freue, GV. Kepplinger D, Salibián-Barrera M, Smucler E. (2019). Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics. 13(4). available online.