Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by MCP or SCAD, with optional additional L2 penalty.
|Author||Patrick Breheny [aut,cre]|
|Date of publication||2017-01-06 23:06:18|
|Maintainer||Patrick Breheny <email@example.com>|
auc: Calculates AUC for cv.ncvsurv objects
cv.ncvreg: Cross-validation for ncvreg
cv.ncvsurv: Cross-validation for ncvsurv
fir: False inclusion rates for ncvreg
heart: Risk factors associated with heart disease
Lung: VA lung cancer data set
ncvreg: Fit an MCP- or SCAD-penalized regression path
ncvreg-internal: Internal ncvreg functions
ncvreg-package: Regularization paths for SCAD- and MCP-penalized regression...
ncvsurv: Fit an MCP- or SCAD-penalized survival model
perm.ncvreg: Permutation fitting for ncvreg
permres: Permute residuals for a fitted ncvreg model
plot.cv.ncvreg: Plots the cross-validation curve from a "cv.ncvreg" object
plot.fir: Plot false inclusion rate curves
plot.ncvreg: Plot coefficients from a "ncvreg" object
plot.ncvsurv.func: Plot survival curve for ncvsurv model
predict: Model predictions based on a fitted "ncvreg" object.
predict.ncvsurv: Model predictions based on a fitted "ncvsurv" object.
prostate: Factors associated with prostate specific antigen
std: Standardizes a design matrix
summary.cv.ncvreg: Summarizing inferences based on cross-validation