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lassopv  

Nonparametric P-Value Estimation for Predictors in Lasso
View on CRAN: Click here


Download and install lassopv package within the R console
Install from CRAN:
install.packages("lassopv")

Install from Github:
library("remotes")
install_github("cran/lassopv")

Install by package version:
library("remotes")
install_version("lassopv", "0.2.0")



Attach the package and use:
library("lassopv")
Maintained by
Lingfei Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-21
Latest Update: 2018-02-22
Description:
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.
How to cite:
Lingfei Wang (2017). lassopv: Nonparametric P-Value Estimation for Predictors in Lasso. R package version 0.2.0, https://cran.r-project.org/web/packages/lassopv. Accessed 06 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:51), 0.1.1 (2017-01-21 02:07), 0.1.3 (2017-02-08 12:46)
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Complete documentation for lassopv
Functions, R codes and Examples using the lassopv R package
Some associated functions: lassopv-package . lassopv . 
Some associated R codes: lassopv.R .  Full lassopv package functions and examples
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