{ "_id": "6a0ff4d0acfb0bcc41c7779a", "Package": "funMoDisco", "Type": "Package", "Title": "Motif Discovery in Functional Data", "Version": "1.1.5", "Date": "2026-04-21", "Authors@R": "c(\nperson(given = c(\"Marzia\", \"Angela\"), family = \"Cremona\", role = \"aut\"),\nperson(given = \"Francesca\", family = \"Chiaromonte\", role = \"aut\"),\nperson(given = \"Jacopo\", family = \"Di Iorio\", role = c(\"aut\", \"cre\"),\nemail = \"jacopo.di.iorio@emory.edu\"),\nperson(given = \"Niccolò\", family = \"Feresini\", role = \"aut\"),\nperson(given = \"Riccardo\", family = \"Lazzarini\", role = \"aut\"))", "Author": "Marzia Angela Cremona [aut], Francesca Chiaromonte [aut],\nJacopo Di Iorio [aut, cre], Niccolò Feresini [aut], Riccardo\nLazzarini [aut]", "Maintainer": "Jacopo Di Iorio ", "Description": "Efficiently implementing two complementary methodologies\nfor discovering motifs in functional data: ProbKMA and\nFunBIalign. Cremona and Chiaromonte (2023) \"Probabilistic\nK-means with Local Alignment for Clustering and Motif Discovery\nin Functional Data\" is a\nprobabilistic K-means algorithm that leverages local alignment\nand fuzzy clustering to identify recurring patterns (candidate\nfunctional motifs) across and within curves, allowing different\nportions of the same curve to belong to different clusters. It\nincludes a family of distances and a normalization to discover\nvarious motif types and learns motif lengths in a data-driven\nmanner. It can also be used for local clustering of misaligned\ndata. Di Iorio, Cremona, and Chiaromonte (2023) \"funBIalign: A\nHierarchical Algorithm for Functional Motif Discovery Based on\nMean Squared Residue Scores\" \napplies hierarchical agglomerative clustering with a functional\ngeneralization of the Mean Squared Residue Score to identify\nmotifs of a specified length in curves. This deterministic\nmethod includes a small set of user-tunable parameters. Both\nalgorithms are suitable for single curves or sets of curves.\nThe package also includes a flexible function to simulate\nfunctional data with embedded motifs, allowing users to\ngenerate benchmark datasets for validating and comparing motif\ndiscovery methods.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "SystemRequirements": "C++20", "LazyData": "true", "VignetteBuilder": "R.rsp", "BuildVignettes": "true", "Language": "en-US", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-22 06:10:26 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-04-22 03:45:22 UTC", "RemoteUrl": "https://github.com/cran/funMoDisco", "RemoteRef": "HEAD", "RemoteSha": "9f85951e9506458197391ee292a73ed3472fc49b", "MD5sum": "5084288686b09760541941da3d6796c3", "_user": "cran", "_type": "src", "_file": "funMoDisco_1.1.5.tar.gz", "_fileid": "eb145863339747e723f3ab46a343246065e0159c00c2b816f5453cb010943cf1", "_filesize": 5292030, 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