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.

Version:	0.2.0
Depends:	R (≥ 2.10)
Imports:	lars, stats
Published:	2018-02-22
Author:	Lingfei Wang
Maintainer:	Lingfei Wang <Lingfei.Wang.github at outlook.com>
License:	GPL-3
Copyright:	Copyright 2016-2018 Lingfei Wang
URL:	https://github.com/lingfeiwang/lassopv
NeedsCompilation:	no
CRAN checks:	lassopv results

Documentation:

Reference manual:

lassopv.pdf

Downloads:

Package source:	lassopv_0.2.0.tar.gz
Windows binaries:	r-devel: lassopv_0.2.0.zip, r-release: lassopv_0.2.0.zip, r-oldrel: lassopv_0.2.0.zip
macOS binaries:	r-release (arm64): lassopv_0.2.0.tgz, r-oldrel (arm64): lassopv_0.2.0.tgz, r-release (x86_64): lassopv_0.2.0.tgz, r-oldrel (x86_64): lassopv_0.2.0.tgz
Old sources:	lassopv archive

Reverse dependencies:

Reverse imports:

trena

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