{ "_id": "6ac0c60c91601a2ad2998b0f", "Package": "OptCirClust", "Type": "Package", "Title": "Circular, Periodic, or Framed Data Clustering: Fast, Optimal,\nand Reproducible", "Version": "0.0.4", "Date": "2021-07-27", "Authors@R": "c(person(\"Tathagata\", \"Debnath\", role = \"aut\", \ncomment = c(ORCID = \"0000-0001-6445-275X\")),\nperson(\"Joe\", \"Song\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6883-6547\"),\nemail = \"joemsong@cs.nmsu.edu\"))", "Author": "Tathagata Debnath [aut]\n(), Joe Song [aut, cre]\n()", "Maintainer": "Joe Song ", "Description": "Fast, optimal, and reproducible clustering algorithms for\ncircular, periodic, or framed data. The algorithms introduced\nhere are based on a core algorithm for optimal framed\nclustering the authors have developed (Debnath & Song 2021)\n. The runtime of these\nalgorithms is O(K N log^2 N), where K is the number of clusters\nand N is the number of circular data points. On a desktop\ncomputer using a single processor core, millions of data points\ncan be grouped into a few clusters within seconds. One can\napply the algorithms to characterize events along circular DNA\nmolecules, circular RNA molecules, and circular genomes of\nbacteria, chloroplast, and mitochondria. One can also cluster\nclimate data along any given longitude or latitude. Periodic\ndata clustering can be formulated as circular clustering. The\nalgorithms offer a general high-performance solution to\ncircular, periodic, or framed data clustering.", "VignetteBuilder": "knitr", "License": "LGPL (>= 3)", "Encoding": "UTF-8", "RoxygenNote": "7.1.1", "NeedsCompilation": "yes", "RdMacros": "Rdpack", "Packaged": { "Date": "2026-10-03 09:02:20 UTC", "User": "root" }, "Config/pak/sysreqs": "libicu-dev", "Repository": "https://joemsong.r-universe.dev", "Date/Publication": "2021-07-28 06:40:05 UTC", "RemoteUrl": "https://github.com/cran/OptCirClust", "RemoteRef": "HEAD", "RemoteSha": "e176681b90869e849aa18cd54aafa265a5df7928", "_user": "joemsong", "_type": "src", "_file": "OptCirClust_0.0.4.tar.gz", "_fileid": "https://r2.ropensci.org/baf8061d24f3b457ff5578c7d0e57ffc93b6f1db29a98a65dff205aa9b63fefe", "_filesize": 1991043, "_sha256": "baf8061d24f3b457ff5578c7d0e57ffc93b6f1db29a98a65dff205aa9b63fefe", "_expires": "2027-01-11T09:08:26.000Z", "_created": "2026-10-03T09:02:20.000Z", "_published": "2026-10-03T09:08:28.581Z", "_jobs": [ { 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