{ "_id": "6a1e86471d7bb097a0a6731d", "Package": "GSD", "Type": "Package", "Title": "Graph Signal Decomposition", "Version": "1.0.0", "Date": "2024-02-04", "Authors@R": "c(person(\"Hyeonglae\", \"Cho\", role = \"aut\", email=\"jhl0580@snu.ac.kr\"),\nperson(\"Hee-Seok\", \"Oh\", role = \"aut\", email = \"heeseok@stats.snu.ac.kr\"),\nperson(\"Donghoh\", \"Kim\", role = c(\"aut\", \"cre\"), email = \"donghoh.kim@gmail.com\"))", "Author": "Hyeonglae Cho [aut], Hee-Seok Oh [aut], Donghoh Kim [aut, cre]", "Maintainer": "Donghoh Kim ", "Description": "Graph signals residing on the vertices of a graph have\nrecently gained prominence in research in various fields. Many\nmethodologies have been proposed to analyze graph signals by\nadapting classical signal processing tools. Recently, several\nnotable graph signal decomposition methods have been proposed,\nwhich include graph Fourier decomposition based on graph\nFourier transform, graph empirical mode decomposition, and\nstatistical graph empirical mode decomposition. This package\nefficiently implements multiscale analysis applicable to\nvarious fields, and offers an effective tool for visualizing\nand decomposing graph signals. For the detailed methodology,\nsee Ortega et al. (2018) ,\nShuman et al. (2013) , Tremblay\net al. (2014)\n,\nand Cho et al. 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