Package index • ncvreg

Model fitting

ncvfit(): Direct interface for nonconvex penalized regression (non-pathwise)
ncvreg(): Fit an MCP- or SCAD-penalized regression path
ncvsurv(): Fit an MCP- or SCAD-penalized survival model
std(): Standardizes a design matrix

assign_fold(): Assign folds for cross-validation
cv.ncvreg() cv.ncvsurv(): Cross-validation for ncvreg/ncvsurv
plot(<cv.ncvreg>): Plots the cross-validation curve from a cv.ncvreg object
summary(<cv.ncvreg>) print(<summary.cv.ncvreg>): Summarizing cross-validation-based inference
AUC(<cv.ncvsurv>): AUC for cv.ncvsurv objects

logLik(<ncvreg>) logLik(<ncvsurv>): Extract Log-Likelihood
predict(<cv.ncvreg>) coef(<cv.ncvreg>) predict(<cv.ncvsurv>) predict(<ncvreg>) coef(<ncvreg>): Model predictions based on a fitted ncvreg object.
predict(<ncvsurv>): Model predictions based on a fitted ncvsurv object.
plot(<ncvreg>): Plot coefficients from a ncvreg object
plot(<ncvsurv.func>): Plot survival curve for ncvsurv model
residuals(<ncvreg>): Extract residuals from a ncvreg or ncvsurv fit

local_mfdr(): Estimate local mFDR for all features
mfdr(): Marginal false discovery rates
plot(<mfdr>): Plot marginal false discovery rate curves
perm.ncvreg(): Permutation fitting for ncvreg
permres(): Permute residuals for a fitted ncvreg model
summary(<ncvreg>) print(<summary.ncvreg>): Summary method for ncvreg objects
intervals(): Projection base test statistics and intervals