sharpPen (version 2.0)

Penalized Data Sharpening for Local Polynomial Regression

Description

Functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties.

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Install

install.packages('sharpPen')

Monthly Downloads

243

Version

2.0

License

Unlimited

Maintainer

D. Wang

Last Published

January 28th, 2025

Functions in sharpPen (2.0)

relsharp_mean

Ridge/Enet/LASSO Sharpening via the Mean

testfun

Functions for Testing Purposes

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_mean_c

Ridge/Enet/LASSO Sharpening via the Mean and then applying the residual sharpening method.

data_sharpening

Penalized data sharpening for Local Linear, Quadratic and Cubic Regression

numericalDerivative

Numerical Derivative of Smooth Function

dpilc_PTW

dpilc_PTW: Local Polynomial Bandwith Estimation with Blockwise Selection and Pointwise Results

lprOperator

Local Polynomial Estimator Matrix Construction

RELsharpening

Ridge/Enet/LASSO Sharpening via the mean/local polynomial regression with large bandwidth/linear regression.

dpilc

Select a Bandwidth for Local Quadratic and Cubic Regression

DR_sharpen

Shape-Constrained Local Linear Regression via Douglas-Rachford

derivOperator

Shape Constraint Matrix Construction

noontemp

Noon Temperatures in Winnipeg, Manitoba

projection_nb

Projection operator for norm balls.

relsharp_linear_c

Ridge/Enet/LASSO Sharpening via the linear regression and then applying the residual sharpening method.

projection_C

Projection operator for rectangle or nonnegative space

relsharp_bigh

Ridge/Enet/LASSO Sharpening via the local polynomial regression with large bandwidth.

relsharpen

Ridge/Enet/LASSO Sharpening via the penalty matrix.