Package: lassopv 0.2.1

lassopv: Nonparametric P-Value Estimation for Predictors in Lasso

Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.

Authors:Lingfei Wang <[email protected]>

lassopv_0.2.1.tar.gz
lassopv_0.2.1.zip(r-4.7)lassopv_0.2.1.zip(r-4.6)lassopv_0.2.1.zip(r-4.5)
lassopv_0.2.1.tgz(r-4.6-any)lassopv_0.2.1.tgz(r-4.5-any)
lassopv_0.2.1.tar.gz(r-4.7-any)lassopv_0.2.1.tar.gz(r-4.6-any)
lassopv_0.2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
lassopv/json (API)

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

Bug tracker:https://github.com/lingfeiwang/lassopv/issues

On CRAN:

Conda:

feature-selectionlassolinear-regressionp-valuevariable-selection

2.30 score 2 stars 5 scripts 187 downloads 1 mentions 1 exports 1 dependencies

Last updated from:0d828c0715. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK92
source / vignettesOK130
linux-release-x86_64OK98
macos-release-arm64OK80
macos-oldrel-arm64OK78
windows-develOK65
windows-releaseOK78
windows-oldrelOK63
wasm-releaseOK87

Exports:lassopv

Dependencies:lars