R: Penalized Data Sharpening for Local Polynomial Regression

# Penalized Data Sharpening for Local Polynomial Regression

## Documentation for package ‘sharpPen’ version 2.0

- DESCRIPTION file

## Help Pages

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