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stepPenal (version 0.2)

Stepwise Forward Variable Selection in Penalized Regression

Description

Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.

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Version

Install

install.packages('stepPenal')

Monthly Downloads

106

Version

0.2

License

GPL-2

Maintainer

Eleni Vradi

Last Published

August 24th, 2018

Functions in stepPenal (0.2)

optimPenaLikL2

Variable selection based on the combined penalty CL2= (1-w)L0 + wL2
tuneParam

Tune parameters w and lamda using the CL penalty
tuneParamCL2

Tune parameters w and lamda using the CL2 penalty
StepPenalL2

Stepwise forward variable selection using penalized regression.
penalBrier

Evaluation of the performance of risk prediction models with binary status response variable.
SimData

Simulate data with normally distributed predictors and binary response
optimPenaLik

Variable selection based on the combined penalty CL= (1-w)L0 + wL1
findROC

Compute the area under the ROC curve
StepPenal

Stepwise forward variable selection using penalized regression.
lassomodel

Fits a lasso model and a lasso followed by a stepAIC algorithm.
objFun

Objective function
stepaic

Stepwise forward variable selection based on the AIC criterion