WIAS-BERLIN/aws: 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, 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. <doi:10.18637/jss.v095.i06>, Usage of the package in neuroimaging is illustrated in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.

Getting started

Package details

AuthorJoerg Polzehl [aut, cre], Felix Anker [ctb]
MaintainerJoerg Polzehl <joerg.polzehl@wias-berlin.de>
LicenseGPL (>=2)
Version2.5-1
URL http://www.wias-berlin.de/people/polzehl/
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("WIAS-BERLIN/aws")
WIAS-BERLIN/aws documentation built on Feb. 4, 2021, 6:23 a.m.