uniLasso: 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) <doi:10.1162/99608f92.c79ff6db> for details.

Version:	2.11
Depends:	glmnet, stats, R (≥ 3.6.0)
Imports:	methods, utils, MASS
Suggests:	testthat
Published:	2026-01-26
DOI:	10.32614/CRAN.package.uniLasso
Author:	Trevor Hastie [aut, cre], Rob Tibshirani [aut], Sourav Chatterjee [aut]
Maintainer:	Trevor Hastie <hastie at stanford.edu>
License:	GPL-2
NeedsCompilation:	no
Materials:	README
CRAN checks:	uniLasso results

Documentation:

Reference manual:

uniLasso.html , uniLasso.pdf

Downloads:

Package source:	uniLasso_2.11.tar.gz
Windows binaries:	r-devel: uniLasso_2.11.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): uniLasso_2.11.tgz, r-oldrel (arm64): uniLasso_2.11.tgz, r-release (x86_64): uniLasso_2.11.tgz, r-oldrel (x86_64): uniLasso_2.11.tgz

Linking:

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