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sharpPen (version 1.9)

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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Version

Install

install.packages('sharpPen')

Monthly Downloads

243

Version

1.9

License

Unlimited

Maintainer

D. Wang

Last Published

October 19th, 2023

Functions in sharpPen (1.9)

projection_C

Projection operator for rectangle or nonnegative space

projection_nb

Projection operator for norm balls.

relsharp_bigh_c

Ridge/Enet/LASSO Sharpening via the local polynomial regression with large bandwidth and then applying the residual sharpening method.

relsharp_bigh

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

relsharp_mean_c

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

relsharp_mean

Ridge/Enet/LASSO Sharpening via the Mean

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.

relsharpen

Ridge/Enet/LASSO Sharpening via the penalty matrix.

testfun

Functions for Testing Purposes

RELsharpening

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

DR_sharpen

Shape-Constrained Local Linear Regression via Douglas-Rachford

dpilc

Select a Bandwidth for Local Quadratic and Cubic Regression

noontemp

Noon Temperatures in Winnipeg, Manitoba

numericalDerivative

Numerical Derivative of Smooth Function

derivOperator

Shape Constraint Matrix Construction

lprOperator

Local Polynomial Estimator Matrix Construction

data_sharpening

Penalized data sharpening for Local Linear, Quadratic and Cubic Regression