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-any)lassopv_0.2.1.zip(r-4.6-any)lassopv_0.2.1.zip(r-4.5-any)
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✨
DESCRIPTION
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 3 scripts 264 downloads 1 mentions 1 exports 1 dependencies

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

TargetResultTimeFilesSyslog
linux-develOK78
source / vignettesOK150
linux-releaseOK83
macos-releaseOK131
macos-oldrelOK168
windows-develOK60
windows-releaseOK66
windows-oldrelOK46
wasm-releaseOK70

Exports:lassopv

Dependencies:lars