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grpreg fits regularization paths for linear, logistic, or Poisson regression models with grouped penalties, such as the group lasso, group MCP, group SCAD, group exponential lasso, and group bridge. The algorithms are based on the idea of either locally approximated coordinate descent or group descent, depending on the penalty. All of the algorithms (with the exception of group bridge) are stable and fast.

To install:

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

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Version

Install

install.packages('grpreg')

Monthly Downloads

4,058

Version

3.1-3

License

GPL-3

Maintainer

Patrick Breheny

Last Published

April 8th, 2018

Functions in grpreg (3.1-3)

cv.grpreg

Cross-validation for grpreg
cv.grpsurv

Cross-validation for grpsurv
birthwt.grpreg

Risk Factors Associated with Low Infant Birth Weight
gBridge

Fit a group bridge regression path
Lung

VA lung cancer data set
AUC.cv.grpsurv

Calculates AUC for cv.grpsurv objects
Birthwt

Risk Factors Associated with Low Infant Birth Weight
grpreg-internal

Internal grpreg functions
grpreg

Fit a group penalized regression path
grpreg-package

Regularization paths for regression models with grouped covariates
logLik.grpreg

logLik method for grpreg
plot.cv.grpreg

Plots the cross-validation curve from a cv.grpreg object
predict.grpsurv

Model predictions based on a fitted "grpsurv" object.
select.grpreg

Select an value of lambda along a grpreg path
summary.cv.grpreg

Summarizing inferences based on cross-validation
plot.grpreg

Plot coefficients from a "grpreg" object
predict.grpreg

Model predictions based on a fitted grpreg object
grpsurv

Fit an group penalized survival model