Title: Nonparametric P-Value Estimation for Predictors in Lasso
Description: The purpose of this package is to estimate 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 that (first) become active earlier tend to be more significant. Null distribution for each predictor is computed analytically under approximation, which aims at efficiency and accuracy for small p-values.
Copyright: Copyright 2016, 2017 Lingfei Wang

Description: Estimate 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 that (first) become active earlier tend to be more significant. Null distribution for each predictor is computed analytically under approximation, which aims at efficiency and accuracy for small p-values.

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.
Copyright: Copyright 2016-2018 Lingfei Wang

Title: high-dimensional inference with the penalized score test
Description: Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of potential confounders.

in high dimensions, by penalizing the effects of nuisance parameters.