# Adaptive Weights Smoothing

## Documentation for package ‘aws’ version 2.5-6

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.

## Help Pages

aws-package | Adaptive Weights Smoothing
AFLocalSigma | 3D variance estimation
aws | AWS for local constant models on a grid
aws-class | Class '"aws"'
aws.gaussian | Adaptive weights smoothing for Gaussian data with variance depending on the mean.
aws.irreg | local constant AWS for irregular (1D/2D) design
aws.segment | Segmentation by adaptive weights for Gaussian models.
aws3Dmask | Auxiliary 3D smoothing routines
aws3Dmaskfull | Auxiliary 3D smoothing routines
awsdata | Extract information from an object of class aws
awslinsd | 3D variance estimation
awsLocalSigma | 3D variance estimation
awssegment-class | Class '"awssegment"'
awstestprop | Propagation condition for adaptive weights smoothing
awsweights | Generate weight scheme that would be used in an additional aws step
binning | Binning in 1D, 2D or 3D
estGlobalSigma | 3D variance estimation
estimateSigmaCompl | 3D variance estimation
extract-method | Methods for Function 'extract' in Package 'aws'
extract-methods | Methods for Function 'extract' in Package 'aws'
gethani | Auxiliary functions (for internal use)
getvofh | Auxiliary functions (for internal use)
ICIcombined | Adaptive smoothing by Intersection of Confidence Intervals (ICI) using multiple windows
ICIsmooth | Adaptive smoothing by Intersection of Confidence Intervals (ICI)
ICIsmooth-class | Class '"ICIsmooth"'
kernsm | Kernel smoothing on a 1D, 2D or 3D grid
kernsm-class | Class '"kernsm"'
lpaws | Local polynomial smoothing by AWS
medianFilter3D | Auxiliary 3D smoothing routines
nlmeans | NLMeans filter in 1D/2D/3D
paws | Adaptive weigths smoothing using patches
pawsm | Adaptive weigths smoothing using patches
pawstestprop | Propagation condition for adaptive weights smoothing
plot-method | Methods for Function 'plot' from package 'graphics' in Package 'aws'
plot-methods | Methods for Function 'plot' from package 'graphics' in Package 'aws'
print-method | Methods for Function 'print' from package 'base' in Package 'aws'
print-methods | Methods for Function 'print' from package 'base' in Package 'aws'
qmeasures | Quality assessment for image reconstructions.
residualSpatialCorr | Auxiliary functions (for internal use)
residualVariance | Auxiliary functions (for internal use)
risk-method | Compute risks characterizing the quality of smoothing results
risk-methods | Compute risks characterizing the quality of smoothing results
show-method | Methods for Function 'show' in Package 'aws'
show-methods | Methods for Function 'show' in Package 'aws'
smooth3D | Auxiliary 3D smoothing routines
smse3 | Adaptive smoothing in orientation space SE(3)
smse3ms | Adaptive smoothing in orientation space SE(3)
sofmchi | Auxiliary functions (for internal use)
summary-method | Methods for Function 'summary' from package 'base' in Package 'aws'
summary-methods | Methods for Function 'summary' from package 'base' in Package 'aws'
TGV_denoising | TV/TGV denoising of image data
TGV_denoising_colour | TV/TGV denoising of image data
TV_denoising | TV/TGV denoising of image data
TV_denoising_colour | TV/TGV denoising of image data
vaws | 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.
vawscov | 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.
vpaws | vector valued version of function 'paws' with homogeneous covariance structure
vpawscov | vector valued version of function 'paws' with homogeneous covariance structure
vpawscov2 | vector valued version of function 'paws' with homogeneous covariance structure