High-Performance Open-Source Archive
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 |
| Reference manual: | uniLasso.html , uniLasso.pdf |
| Package source: | uniLasso_2.11.tar.gz |
| Windows binaries: | r-devel: uniLasso_2.11.zip, r-release: uniLasso_2.11.zip, r-oldrel: uniLasso_2.11.zip |
| 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 |
Please use the canonical form https://CRAN.R-project.org/package=uniLasso to link to this page.
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.