Sparse modelling with grouped and correlated features

Scope

The R package corila implements sparse modelling with grouped and correlated features allowing for privileged information (Rauschenberger, 2026).

Installation

Install the latest development version from GitHub:

#install.packages("remotes")
remotes::install_github("rauschenberger/corila")

Usage

Use the function cv.corila to model an outcome (n-dimensional vector y) based on many predictors (n x p matrix X) that are structured by groups (e.g., p-dimensional vector group) and potentially split into primary and auxiliary predictors (p-dimensional vector primary). See the vignette for detailed examples.

library(corila)
#load(toydata)
object <- cv.corila(x = x_train, y = y_train, group = group, primary = primary)
coef(object)
predict(object, newx = x_test)

Reference

Armin Rauschenberger (2026). "Sparse modelling with grouped and correlated features allowing for privileged information". Manuscript in preparation.

(Presented at SIS-FENStatS in Rome, Italy, on 25 June 2026.)

Disclaimer

This public repository is on a personal GitHub account, but it has private pull mirrors on two institutional GitLab instances (see LIH and LCSB).

Large-language models (mainly Claude Sonnet 4.6 and GTP-5.4) were used for reviewing R code and documentation and for adapting configuration files (.yaml).

Copyright © 2025 Armin Rauschenberger; Luxembourg Institute of Health (LIH), Department of Medical Informatics (DMI), Bioinformatics and Artificial Intelligence (BioAI); University of Luxembourg, Luxembourg Centre for Systems Biomedicine (LCSB), Biomedical Data Science (BDS). This R package is distributed under a GPL 3 license.