Regularization Paths for SCAD and MCP Penalized Regression Models
AUC for cv.ncvsurv objects
Cross-validation for ncvreg/ncvsurv
Estimate local mFDR for all features
Extract Log-Likelihood
Marginal false discovery rates
Direct interface for nonconvex penalized regression (non-pathwise)
ncvreg: Regularization Paths for SCAD and MCP Penalized Regression Mod...
Fit an MCP- or SCAD-penalized regression path
Fit an MCP- or SCAD-penalized survival model
Permutation fitting for ncvreg
Permute residuals for a fitted ncvreg model
Plots the cross-validation curve from a cv.ncvreg object
Plot marginal false discovery rate curves
Plot coefficients from a ncvreg object
Plot survival curve for ncvsurv model
Model predictions based on a fitted ncvreg object.
Model predictions based on a fitted ncvsurv object.
Extract residuals from a ncvreg or ncvsurv fit
Standardizes a design matrix
Summarizing cross-validation-based inference
Summary method for ncvreg objects
Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388> or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.
Useful links