Package: glmnetSE 0.0.1

Sebastian Bahr

glmnetSE: Add Nonparametric Bootstrap SE to 'glmnet' for Selected Coefficients (No Shrinkage)

Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011) <doi:10.18637/jss.v039.i05>.

Authors:Sebastian Bahr [cre, aut]

glmnetSE_0.0.1.tar.gz
glmnetSE_0.0.1.zip(r-4.5)glmnetSE_0.0.1.zip(r-4.4)glmnetSE_0.0.1.zip(r-4.3)
glmnetSE_0.0.1.tgz(r-4.5-any)glmnetSE_0.0.1.tgz(r-4.4-any)glmnetSE_0.0.1.tgz(r-4.3-any)
glmnetSE_0.0.1.tar.gz(r-4.5-noble)glmnetSE_0.0.1.tar.gz(r-4.4-noble)
glmnetSE_0.0.1.tgz(r-4.4-emscripten)glmnetSE_0.0.1.tgz(r-4.3-emscripten)
glmnetSE.pdf |glmnetSE.html✨
glmnetSE/json (API)
NEWS

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

Bug tracker:https://github.com/sebastianbahr/glmnetse/issues

On CRAN:

Conda:

3.00 score 2 stars 2 scripts 160 downloads 1 exports 11 dependencies

Last updated 3 years agofrom:dfa699d9d3. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-winNOTEMar 25 2025
R-4.5-macNOTEMar 25 2025
R-4.5-linuxNOTEMar 25 2025
R-4.4-winNOTEMar 25 2025
R-4.4-macNOTEMar 25 2025
R-4.4-linuxNOTEMar 25 2025
R-4.3-winNOTEMar 25 2025
R-4.3-macNOTEMar 25 2025

Exports:glmnetSE

Dependencies:bootcodetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival