Learn R Programming

⚠️There's a newer version (2.5-6) of this package.Take me there.

aws (version 1.9-3)

Adaptive Weights Smoothing

Description

The package contains R-functions implementing the Propagation-Separation Approach to adaptive smoothing as described in J. Polzehl and V. Spokoiny (2006), Propagation-Separation Approach for Local Likelihood Estimation, Prob. Theory and Rel. Fields, 135(3):335--362. and J. Polzehl and V. Spokoiny (2004) Spatially adaptive regression estimation: Propagation-separation approach, WIAS-Preprint 998.

Copy Link

Version

Install

install.packages('aws')

Monthly Downloads

637

Version

1.9-3

License

GPL (>= 2)

Maintainer

Joerg Polzehl

Last Published

March 18th, 2013

Functions in aws (1.9-3)

Binning in 1D, 2D or 3D

Adaptive weights smoothing for Gaussian data with variance depending on the mean.

Adaptive smoothing by Intersection of Confidence Intervals (ICI)

Propagation condition for adaptive weights smoothing

awssegment-class

Class "awssegment"

Adaptive smoothing by Intersection of Confidence Intervals (ICI) using multiple windows

AWS for local constant models on a grid

Kernel smoothing on a 1D, 2D or 3D grid

Extract information from an object of class aws

summary-methods

Methods for Function `summary' from package 'base' in Package `aws'

ICIsmooth-class

Class "ICIsmooth"

Methods for Function `show' in Package `aws'

Segmentation by adaptive weights for Gaussian models.

Compute risks characterizing the quality of smoothing results

Adaptive Weights Smoothing

Local polynomial smoothing by AWS

local constant AWS for irregular (1D/2D) design

Methods for Function `plot' from package 'graphics' in Package `aws'

extract-methods

Methods for Function extract in Package aws

Class "kernsm"

Methods for Function `print' from package 'base' in Package `aws'