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)
Maintainer: Joerg Polzehl < joerg.polzehl at wias-berlin.de >
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.
Initially submitted on | February 3 2020 2:54PM |
Last updated on | October 15 2020 2:50PM |
Package type | standard |
Source GitHub | https://github.com/WIAS-BERLIN/aws GitHub |
Neuroconductor GitHub | https://github.com/neuroconductor/aws GitHub |
Reverse Imports | qMRI (2.4), dti (2.4.1), fmri (2.4) |