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ncvreg fits regularization paths for linear, logistic, or Poisson regression models with nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty. Coordinate descent algorithms are used to fit the models.

To install:

  • the latest released version: install.packages("ncvreg")
  • the latest version (requires devtools): install_github("pbreheny/ncvreg")

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Version

Install

install.packages('ncvreg')

Monthly Downloads

5,280

Version

3.8-0

License

GPL-3

Maintainer

Patrick Breheny

Last Published

January 6th, 2017

Functions in ncvreg (3.8-0)

ncvreg-internal

Internal ncvreg functions
ncvreg

Fit an MCP- or SCAD-penalized regression path
ncvreg-package

Regularization paths for SCAD- and MCP-penalized regression models
cv.ncvreg

Cross-validation for ncvreg
AUC.cv.ncvsurv

Calculates AUC for cv.ncvsurv objects
ncvsurv

Fit an MCP- or SCAD-penalized survival model
fir

False inclusion rates for ncvreg
cv.ncvsurv

Cross-validation for ncvsurv
heart

Risk factors associated with heart disease
Lung

VA lung cancer data set
prostate

Factors associated with prostate specific antigen
std

Standardizes a design matrix
predict.ncvsurv

Model predictions based on a fitted "ncvsurv" object.
plot.fir

Plot false inclusion rate curves
predict.ncvreg

Model predictions based on a fitted "ncvreg" object.
plot.cv.ncvreg

Plots the cross-validation curve from a "cv.ncvreg" object
plot.ncvsurv.func

Plot survival curve for ncvsurv model
plot.ncvreg

Plot coefficients from a "ncvreg" object
permres

Permute residuals for a fitted ncvreg model
perm.ncvreg

Permutation fitting for ncvreg
summary.cv.ncvreg

Summarizing inferences based on cross-validation