Package aws details

Adaptive Weights Smoothing

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. .

Maintainer: Joerg Polzehl < joerg.polzehl at wias-berlin.de >

Documentation

From within R, enter citation('aws')

To cite aws in publications use:

Joerg Polzehl, Vladimir Spokoiny (2006). Propagation-separation
approach for local likelihood estimation, Probab. Theory Related
Fields, 135(3), 335-362.

Joerg Polzehl, Kostas Papafitsoros and Karsten Tabelow (2018).
Patch-wise adaptive weights smoothing, WIAS Preprint 2520,
DOI:10.20347/WIAS.PREPRINT.2520

To see these entries in BibTeX format, use 'print(,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

If you have any problems with this package you can open a new issue or check the already existing ones here.

To install this package, start R and enter:

source("https://neuroconductor.org/neurocLite.R")

# Default Install
neuro_install('aws')

# from GitHub
neuro_install('aws', release = "stable", release_repo = "github")
neuro_install('aws', release = "current", release_repo = "github")

More detailed installation instructions can be found here.