Univariate-Guided Sparse Regression

Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class 'glmnet', so that all of the methods for 'glmnet' are available. See Chatterjee, Hastie and Tibshirani (2025) for details.

uniLasso (Univariate guided Lasso)

Fits a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class glmnet, so that all of the methods for glmnet are available. See doi:10.48550/arXiv.2501.18360 for details.

Installation

To install the uniLasso R package directly from github, run the following in R:

library(devtools)
install_github(repo="trevorhastie/uniLasso")