{ "_id": "6ab8ff2499f7aa8c6aa3975b", "Package": "aws", "Version": "2.5-7", "Date": "2026-07-13", "Title": "Adaptive Weights Smoothing", "Authors@R": "c(person(\"Joerg\",\"Polzehl\",role=c(\"aut\",\"cre\"),email=\"joerg.polzehl@wias-berlin.de\"),person(\"Felix\",\"Anker\",role=c(\"ctb\")))", "Author": "Joerg Polzehl [aut, cre], Felix Anker [ctb]", "Maintainer": "Joerg Polzehl ", "Description": "We provide a collection of R-functions implementing\nadaptive smoothing procedures in 1D, 2D and 3D. This includes\nthe Propagation-Separation Approach to adaptive smoothing, the\nIntersecting Confidence Intervals (ICI), variational approaches\nand a non-local means filter. The package is described in\ndetail in Polzehl J, Papafitsoros K, Tabelow K (2020).\nPatch-Wise Adaptive Weights Smoothing in R. Journal of\nStatistical Software, 95(6), 1-27. ,\nUsage of the package in MR imaging is illustrated in Polzehl\nand Tabelow (2023), Magnetic Resonance Brain Imaging, 2nd Ed.\nAppendix A, Springer, Use R! Series.\n.", "License": "GPL (>= 2)", "Copyright": "This package is Copyright (C) 2005-2024 Weierstrass\nInstitute for Applied Analysis and Stochastics.", "URL": "https://www.wias-berlin.de/people/polzehl/", "RoxygenNote": "5.0.1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-09-27 11:25:36 UTC", "User": "root" }, "Config/pak/sysreqs": "libgsl0-dev", "Repository": "https://jpolzehl.r-universe.dev", "Date/Publication": "2026-07-13 17:30:02 UTC", "RemoteUrl": "https://github.com/cran/aws", "RemoteRef": "HEAD", "RemoteSha": "c117fde7914e21f0e5ae79cae38503ce6e79930d", "_user": "jpolzehl", "_type": "src", "_file": "aws_2.5-7.tar.gz", "_fileid": "https://r2.ropensci.org/d8d48b74b8536c3dfc7902c1fe39963f24a978da4101a19ec113624beaeff163", "_filesize": 907819, "_sha256": "d8d48b74b8536c3dfc7902c1fe39963f24a978da4101a19ec113624beaeff163", "_expires": "2027-01-05T11:33:54.000Z", "_created": "2026-09-27T11:25:36.000Z", "_published": "2026-09-27T11:33:56.798Z", "_jobs": [ { "job": 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