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aws (version 2.4-0)

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. Usage of the package is also described in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series. .

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Version

Install

install.packages('aws')

Monthly Downloads

917

Version

2.4-0

License

GPL (>= 2)

Maintainer

Joerg Polzehl

Last Published

January 21st, 2020

Functions in aws (2.4-0)

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

Segmentation by adaptive weights for Gaussian models.

AWS for local constant models on a grid

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

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

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

Binning in 1D, 2D or 3D

ICIsmooth-class

Class "ICIsmooth"

Adaptive weigths smoothing using patches

Adaptive smoothing by Intersection of Confidence Intervals (ICI)

3D variance estimation

Extract information from an object of class aws

TV/TGV denoising of image data

awssegment-class

Class "awssegment"

Compute risks characterizing the quality of smoothing results

Propagation condition for adaptive weights smoothing

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

Quality assessment for image reconstructions.

Class "kernsm"

vector valued version of function paws with homogeneous covariance structure

Methods for Function `show' in Package `aws'

extract-methods

Methods for Function extract in Package aws

Auxiliary functions (for internal use)

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

Auxiliary 3D smoothing routines

Adaptive smoothing in orientation space SE(3)

summary-methods

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

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

Local polynomial smoothing by AWS

NLMeans filter in 1D/2D/3D

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.