Penalized Data Sharpening for Local Polynomial Regression

Documentation for package ‘sharpPen’ version 2.0

data_sharpening	Penalized data sharpening for Local Linear, Quadratic and Cubic Regression
derivOperator	Shape Constraint Matrix Construction
dpilc	Select a Bandwidth for Local Quadratic and Cubic Regression
dpilc_PTW	dpilc_PTW: Local Polynomial Bandwith Estimation with Blockwise Selection and Pointwise Results
DR_sharpen	Shape-Constrained Local Linear Regression via Douglas-Rachford
lprOperator	Local Polynomial Estimator Matrix Construction
noontemp	Noon Temperatures in Winnipeg, Manitoba
numericalDerivative	Numerical Derivative of Smooth Function
projection_C	Projection operator for rectangle or nonnegative space
projection_nb	Projection operator for norm balls.
relsharpen	Ridge/Enet/LASSO Sharpening via the penalty matrix.
RELsharpening	Ridge/Enet/LASSO Sharpening via the mean/local polynomial regression with large bandwidth/linear regression.
relsharp_bigh	Ridge/Enet/LASSO Sharpening via the local polynomial regression with large bandwidth.
relsharp_bigh_c	Ridge/Enet/LASSO Sharpening via the local polynomial regression with large bandwidth and then applying the residual sharpening method.
relsharp_linear	Ridge/Enet/LASSO Sharpening via the linear regression.
relsharp_linear_c	Ridge/Enet/LASSO Sharpening via the linear regression and then applying the residual sharpening method.
relsharp_mean	Ridge/Enet/LASSO Sharpening via the Mean
relsharp_mean_c	Ridge/Enet/LASSO Sharpening via the Mean and then applying the residual sharpening method.
testfun	Functions for Testing Purposes