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aws (version 2.3-0)

Adaptive Weights Smoothing

Description

We provide a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation Approach to adaptive smoothing as described in "J. Polzehl and V. Spokoiny (2006) ", "J. Polzehl and V. Spokoiny (2004) " and "J. Polzehl, K. Papafitsoros, K. Tabelow (2018) ", the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.

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Version

Install

install.packages('aws')

Monthly Downloads

1,012

Version

2.3-0

License

GPL (>= 2)

Maintainer

Joerg Polzehl

Last Published

November 15th, 2019

Functions in aws (2.3-0)

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

Binning in 1D, 2D or 3D

ICIsmooth-class

Class "ICIsmooth"

extract-methods

Methods for Function extract in Package aws

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

Class "kernsm"

Local polynomial smoothing by AWS

AWS for local constant models on a grid

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

NLMeans filter in 1D/2D/3D

Propagation condition for adaptive weights smoothing

summary-methods

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

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

vector valued version of function aws The function implements the propagation separation approach to nonparametric smoothing (formerly introduced as Adaptive weights smoothing) for varying coefficient likelihood models with vector valued response on a 1D, 2D or 3D grid.

Extract information from an object of class aws

Quality assessment for image reconstructions.

awssegment-class

Class "awssegment"

Adaptive weigths smoothing using patches

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

Generate weight scheme that would be used in an additional aws step

Compute risks characterizing the quality of smoothing results

vector valued version of function paws with homogeneous covariance structure

Methods for Function `show' in Package `aws'

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

Segmentation by adaptive weights for Gaussian models.

Adaptive smoothing by Intersection of Confidence Intervals (ICI)

TV/TGV denoising of image data