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aws (version 2.5-5)

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, the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter. The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. , Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. .

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Version

Install

install.packages('aws')

Monthly Downloads

637

Version

2.5-5

License

GPL (>= 2)

Maintainer

Joerg Polzehl

Last Published

February 7th, 2024

Functions in aws (2.5-5)

AWS for local constant models on a grid

TV/TGV denoising of image data

tools:::Rd_package_title("aws")

ICIsmooth-class

Class "ICIsmooth"

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

Segmentation by adaptive weights for Gaussian models.

Adaptive smoothing by Intersection of Confidence Intervals (ICI)

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

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

Propagation condition for adaptive weights smoothing

Binning in 1D, 2D or 3D

awssegment-class

Class "awssegment"

Class "kernsm"

Extract information from an object of class aws

3D variance estimation

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

extract-methods

Methods for Function extract in Package aws

Auxiliary functions (for internal use)

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

Local polynomial smoothing by AWS

Quality assessment for image reconstructions.

Methods for Function `show' in Package `aws'

Adaptive smoothing in orientation space SE(3)

Compute risks characterizing the quality of smoothing results

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

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

NLMeans filter in 1D/2D/3D

Auxiliary 3D smoothing routines

Adaptive weigths smoothing using patches

summary-methods

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

vector valued version of function paws with homogeneous covariance structure

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.