R: Stepwise Forward Variable Selection in Penalized Regression

# Stepwise Forward Variable Selection in Penalized Regression

## Documentation for package ‘stepPenal’ version 0.2

- DESCRIPTION file

## Help Pages

findROC | Compute the area under the ROC curve
lassomodel | Fits a lasso model and a lasso followed by a stepAIC algorithm.
objFun | Objective function
optimPenaLik | Variable selection based on the combined penalty CL= (1-w)L0 + wL1
optimPenaLikL2 | Variable selection based on the combined penalty CL2= (1-w)L0 + wL2
penalBrier | Evaluation of the performance of risk prediction models with binary status response variable.
SimData | Simulate data with normally distributed predictors and binary response
stepaic | Stepwise forward variable selection based on the AIC criterion
StepPenal | Stepwise forward variable selection using penalized regression.
StepPenalL2 | Stepwise forward variable selection using penalized regression.
tuneParam | Tune parameters w and lamda using the CL penalty
tuneParamCL2 | Tune parameters w and lamda using the CL2 penalty