Regularization Paths for Regression Models with Grouped Covariates
Calculates AUC for cv.grpsurv objects
Cross-validation for grpreg/grpsurv
Expand feature matrix using basis splines
Fit a group bridge regression path
Generate nonlinear example data
grpreg: Regularization Paths for Regression Models with Grouped Covari...
Fit a group penalized regression path
Fit an group penalized survival model
logLik method for grpreg
Plot spline curve for a fitted additive model
Plots the cross-validation curve from a cv.grpreg object
Plot coefficients from a "grpreg" object
Plot survival curve for grpsurv model
Model predictions based on a fitted grpreg object
Model predictions for grpsurv objects
Extract residuals from a grpreg or grpsurv fit
Select an value of lambda along a grpreg path
Summarizing inferences based on cross-validation
Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox 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. For more information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>, Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015) <doi:10.1111/biom.12300>, or visit the package homepage <https://pbreheny.github.io/grpreg/>.
Useful links