Package: aws 2.5-6
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 MR imaging is illustrated in Polzehl and Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series. <doi:10.1007/978-3-031-38949-8>.
Authors:
aws_2.5-6.tar.gz
aws_2.5-6.zip(r-4.5)aws_2.5-6.zip(r-4.4)aws_2.5-6.zip(r-4.3)
aws_2.5-6.tgz(r-4.4-x86_64)aws_2.5-6.tgz(r-4.4-arm64)aws_2.5-6.tgz(r-4.3-x86_64)aws_2.5-6.tgz(r-4.3-arm64)
aws_2.5-6.tar.gz(r-4.5-noble)aws_2.5-6.tar.gz(r-4.4-noble)
aws_2.5-6.tgz(r-4.4-emscripten)aws_2.5-6.tgz(r-4.3-emscripten)
aws.pdf |aws.html✨
aws/json (API)
# Install 'aws' in R: |
install.packages('aws', repos = c('https://jpolzehl.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:12f22ac737. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win-x86_64 | OK | Oct 31 2024 |
R-4.5-linux-x86_64 | OK | Oct 31 2024 |
R-4.4-win-x86_64 | OK | Oct 31 2024 |
R-4.4-mac-x86_64 | OK | Oct 31 2024 |
R-4.4-mac-aarch64 | OK | Oct 31 2024 |
R-4.3-win-x86_64 | OK | Oct 31 2024 |
R-4.3-mac-x86_64 | OK | Oct 31 2024 |
R-4.3-mac-aarch64 | OK | Oct 31 2024 |
Exports:AFLocalSigmaawsaws.gaussianaws.irregaws.segmentaws3Dmaskaws3DmaskfullawsdataawslinsdawsLocalSigmaawstestpropawsweightsbinningestGlobalSigmaestimateSigmaComplextractgethanigetvofhICIcombinedICIsmoothkernsmlpawsmedianFilter3DnlmeanspawspawstestpropplotprintqmeasuresresidualSpatialCorrresidualVarianceriskshowsmooth3Dsmse3smse3mssofmchisummaryTGV_denoisingTGV_denoising_colourTV_denoisingTV_denoising_colourvawsvawscovvpawsvpawscovvpawscov2
Dependencies:awsMethodsgsl