R: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

# A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

## Documentation for package ‘smoothedLasso’ version 1.6

- DESCRIPTION file

## Help Pages

crossvalidation | Perform cross validation to select the regularization parameter.
elasticNet | Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the elastic net.
fusedLasso | Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the fused Lasso.
graphicalLasso | Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the graphical Lasso.
minimizeFunction | Minimize the objective function of an unsmoothed or smoothed regression operator with respect to betavector using BFGS.
minimizeSmoothedSequence | Minimize the objective function of a smoothed regression operator with respect to betavector using the progressive smoothing algorithm.
objFunction | Auxiliary function to define the objective function of an L1 penalized regression operator.
objFunctionGradient | Auxiliary function which computes the (non-smooth) gradient of an L1 penalized regression operator.
objFunctionSmooth | Auxiliary function to define the objective function of the smoothed L1 penalized regression operator.
objFunctionSmoothGradient | Auxiliary function which computes the gradient of the smoothed L1 penalized regression operator.
prsLasso | Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso for polygenic risk scores (prs).
standardLasso | Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso.