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.

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.

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

Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of nuisance parameters.

High-Dimensional Inference with the Penalized Score Test

L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting - Is fast - Can
choose which coefficients to penalize - Can take X'X and X'y
instead of X and y

L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting is fast. This
implementation can choose which coefficients to penalize. It
support coefficient-specific penalities and it can take X'X and
X'y instead of X and y.

L1 Regularized Regression (Lasso) Solver using the Cyclic
Coordinate Descent Algorithm aka Lasso Shooting
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting is fast. This
implementation can choose which coefficients to penalize. It
support coefficient-specific penalties and it can take X'X and
X'y instead of X and y.