{ "_id": "6a1f3690b401979e7342897c", "Package": "SVMD", "Type": "Package", "Title": "Spearman Variational Mode Decomposition", "Version": "0.1.0", "Authors@R": "c(person(\"Dr. Himadri Shekhar\", \"Roy\", role = c(\"aut\",\"cre\"), email = \"himadriiasri@gmail.com\"),\nperson(\" Dr. Ranjit Kumar\", \"Paul\", role = \"aut\", email = \"ranjitstat@gmail.com\"),\nperson(\" Dr. Chiranjit\", \"Mazumder\", role = \"aut\", email = \"majumder.chira@gmail.com\"),\nperson(\" Dr. Kamalika\", \"Nath\", role = \"aut\", email = \"kamalikanath@gmail.com\"),\nperson(\" Dr. Prakash\", \"Kumar\", role = \"aut\", email = \"prakash289111@gmail.com\"))", "Author": "Dr. Himadri Shekhar Roy [aut, cre], Dr. Ranjit Kumar Paul\n[aut], Dr. Chiranjit Mazumder [aut], Dr. Kamalika Nath [aut],\nDr. Prakash Kumar [aut]", "Maintainer": "Dr. Himadri Shekhar Roy ", "Description": "In practice, it is difficult to determine the number of\ndecomposition modes, K, for Variational Mode Decomposition\n(VMD). To overcome this issue, this study offers Spearman\nVariational Mode Decomposition (SVMD), a method that uses the\nSpearman correlation coefficient to calculate the ideal mode\nnumber. Unlike the Pearson correlation coefficient, which only\nreturns a perfect value when X and Y are linearly connected,\nthe Spearman correlation can be calculated without knowing the\nprobability distributions of X and Y. The Spearman correlation\ncoefficient, also called Spearman's rank correlation\ncoefficient, is a subset of a wider correlation coefficient. As\nVMD decomposes a signal, the Spearman correlation coefficient\nbetween the reconstructed and original sequences rises as the\nmode number K increases. Once the signal has been fully\ndecomposed, subsequent increases in K cause the correlation to\ngradually level off. When the correlation reaches a specific\nlevel, VMD is said to have adequately decomposed the signal.\nNumerous experiments revealed that a threshold of 0.997\nproduces the best denoising effect, so the threshold is set at\n0.997. This package has been developed using concept of Yang et\nal. (2021).", "License": "GPL-3", "Encoding": "UTF-8", "NeedsCompilation": "no", "RoxygenNote": "7.3.1", "Packaged": { "Date": "2026-05-15 06:14:35 UTC", "User": "root" }, "Repository": "https://himadriiasri.r-universe.dev", "Date/Publication": "2024-09-17 02:32:53 UTC", "RemoteUrl": "https://github.com/cran/SVMD", "RemoteRef": "HEAD", "RemoteSha": "d585dad91441b801f5308e0cb92c234542dff657", "MD5sum": "04dbbc49997788adfa7c5114fc5be4fc", "_user": "himadriiasri", "_type": "src", "_file": "SVMD_0.1.0.tar.gz", "_fileid": "e5457391a65236f00b529c031f20386c109933bfc37d2aeeddd035377d6febc2", "_filesize": 78984, "_sha256": "e5457391a65236f00b529c031f20386c109933bfc37d2aeeddd035377d6febc2", "_created": "2026-05-15T06:14:35.000Z", "_published": "2026-06-02T20:01:20.933Z", "_distro": "noble", "_jobs": [ { "job": 79160613461, "time": 115, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7011255209" }, { "job": 79160614398, "time": 97, "config": "linux-release-x86_64", "r": 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