A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

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Documentation for package ‘smoothedLasso’ version 1.6

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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.