Univariate-Guided Sparse Regression R package uniLasso version 2.11 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) <doi:10.1162/99608f92.c79ff6db> for details. Software Depends: glmnet, stats, R (>= 3.6.0) Imports: methods, utils, MASS Suggests: testthat Trevor Hastie <hastie@stanford.edu> Comprehensive R Archive Network (CRAN) Trevor Hastie [aut, cre], Rob Tibshirani [aut], Sourav Chatterjee [aut] GPL-2 2026-01-26 application/tgz https://CRAN.R-project.org/package=uniLasso doi:10.32614/CRAN.package.uniLasso