{ "_id": "6a1eb615b25058d4daaddf0d", "Package": "ADPF", "Title": "Use Least Squares Polynomial Regression and Statistical Testing\nto Improve Savitzky-Golay", "Version": "0.0.1", "Authors@R": "c(\nperson(\"Phillip\", \"Barak\", email = \"phillip.barak@wisc.edu\", role = \"aut\"),\nperson(\"Samuel\", \"Kruse\", email = \"samdkruse@gmail.com\", role = c(\"cre\",\"aut\")))", "Maintainer": "Samuel Kruse ", "Description": "This function takes a vector or matrix of data and smooths\nthe data with an improved Savitzky Golay transform. The\nSavitzky-Golay method for data smoothing and differentiation\ncalculates convolution weights using Gram polynomials that\nexactly reproduce the results of least-squares polynomial\nregression. Use of the Savitzky-Golay method requires\nspecification of both filter length and polynomial degree to\ncalculate convolution weights. For maximum smoothing of\nstatistical noise in data, polynomials with low degrees are\ndesirable, while a high polynomial degree is necessary for\naccurate reproduction of peaks in the data. Extension of the\nleast-squares regression formalism with statistical testing of\nadditional terms of polynomial degree to a heuristically chosen\nminimum for each data window leads to an adaptive-degree\npolynomial filter (ADPF). Based on noise reduction for data\nthat consist of pure noise and on signal reproduction for data\nthat is purely signal, ADPF performed nearly as well as the\noptimally chosen fixed-degree Savitzky-Golay filter and\noutperformed sub-optimally chosen Savitzky-Golay filters. For\nsynthetic data consisting of noise and signal, ADPF\noutperformed both optimally chosen and sub-optimally chosen\nfixed-degree Savitzky-Golay filters. See Barak, P. (1995)\n for more information.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "6.0.1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 10:39:50 UTC", "User": "root" }, "Author": "Phillip Barak [aut], Samuel Kruse [cre, aut]", "Repository": "https://samdkruse.r-universe.dev", "Date/Publication": "2017-09-13 16:50:51 UTC", "RemoteUrl": "https://github.com/cran/ADPF", "RemoteRef": "HEAD", "RemoteSha": "9769bf223fb2af4cf46a76b31fb2210ad214953c", "MD5sum": "ebac4a39a2c6283e5dd8ac16f3c5c191", "_user": "samdkruse", "_type": "src", "_file": "ADPF_0.0.1.tar.gz", "_fileid": "c7948ce9477d6d51e78d0a96190c34e1609c7d691942fe1c15637950712b2905", "_filesize": 158010, "_sha256": "c7948ce9477d6d51e78d0a96190c34e1609c7d691942fe1c15637950712b2905", "_created": "2026-06-02T10:39:50.000Z", "_published": "2026-06-02T10:53:09.302Z", "_distro": "noble", "_jobs": [ { "job": 79052560489, "time": 121, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", 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