Efficient algorithms for fitting the regularization path of linear or logistic regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge.
|Date of publication||2016-07-11 22:56:52|
|Maintainer||Patrick Breheny <firstname.lastname@example.org>|
auc: Calculates AUC for cv.grpsurv objects
Birthwt: Risk Factors Associated with Low Infant Birth Weight
birthwt.grpreg: Risk Factors Associated with Low Infant Birth Weight
cv.grpreg: Cross-validation for grpreg
cv.grpsurv: Cross-validation for grpsurv
gBridge: Fit a group bridge regression path
grpreg: Fit a group penalized regression path
grpreg-internal: Internal grpreg functions
grpreg-package: Regularization paths for regression models with grouped...
grpsurv: Fit an group penalized survival model
logLik.grpreg: logLik method for grpreg
Lung: VA lung cancer data set
plot.cv.grpreg: Plots the cross-validation curve from a 'cv.grpreg' object
plot.grpreg: Plot coefficients from a "grpreg" object
predict: Model predictions based on a fitted 'grpreg' object
predict.grpsurv: Model predictions based on a fitted "grpsurv" object.
select.grpreg: Select an value of lambda along a grpreg path
summary.cv.grpreg: Summarizing inferences based on cross-validation