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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: not available, 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 |
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These binaries (installable software) and packages are in development.
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