Penalized Data Sharpening for Local Polynomial Regression

Documentation for package ‘sharpPen’ version 1.7

data_sharpening	Penalized data sharpening for Local Linear, Quadratic and Cubic Regression
dpilc	Select a Bandwidth for Local Quadratic and Cubic Regression
getA	Local Polynomial Estimator Matrix Construction
getB	Shape Constraint Matrix Construction
noontemp	Noon Temperatures in Winnipeg, Manitoba
numericalDerivative	Numerical Derivative of Smooth Function
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