Package: uniLasso 2.11

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.

Authors:Trevor Hastie [aut, cre], Rob Tibshirani [aut], Sourav Chatterjee [aut]

uniLasso_2.11.tar.gz
uniLasso_2.11.zip(r-4.7)uniLasso_2.11.zip(r-4.6)uniLasso_2.11.zip(r-4.5)
uniLasso_2.11.tgz(r-4.6-any)uniLasso_2.11.tgz(r-4.5-any)
uniLasso_2.11.tar.gz(r-4.7-any)uniLasso_2.11.tar.gz(r-4.6-any)
uniLasso_2.11.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
uniLasso/json (API)

# Install 'uniLasso' in R:
install.packages('uniLasso', repos = c('https://trevorhastie.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/trevorhastie/unilasso/issues

On CRAN:

Conda:

5.02 score 25 stars 21 scripts 199 downloads 13 exports 11 dependencies

Last updated from:0f0a44ab49. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK141
source / vignettesOK172
linux-release-x86_64OK140
macos-release-arm64OK101
macos-oldrel-arm64OK97
windows-develOK94
windows-releaseOK96
windows-oldrelOK99
wasm-releaseOK119

Exports:ci.uniRegcv.uniLassocv.uniRegpolish.uniLassoprint.cv.uniRegsimulate_counterexamplesimulate_Gaussiansimulate_twoclasssimulate_uniLassouniCoefuniInfouniLassouniReg

Dependencies:codetoolsforeachglmnetiteratorslatticeMASSMatrixRcppRcppEigenshapesurvival