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 218 downloads 13 exports 11 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK139
source / vignettesOK168
linux-release-x86_64OK190
macos-release-arm64OK122
macos-oldrel-arm64OK110
windows-develOK89
windows-releaseOK97
windows-oldrelOK91
wasm-releaseOK142

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

Dependencies:codetoolsforeachglmnetiteratorslatticeMASSMatrixRcppRcppEigenshapesurvival