{ "_id": "6a1041d0acfb0bcc41c9d96f", "Package": "bigmds", "Title": "Multidimensional Scaling for Big Data", "Version": "3.0.0", "Authors@R": "c(person(given = \"Cristian\",\nfamily = \"Pachón García\",\nrole = c(\"aut\", \"cre\"),\nemail = \"cc.pachon@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9518-4874\")),\nperson(given = \"Pedro\",\nfamily = \"Delicado\",\nrole = c(\"aut\"),\nemail = \"pedro.delicado@upc.edu\",\ncomment = c(ORCID = \"0000-0003-3933-4852\")))", "Description": "MDS is a statistic tool for reduction of dimensionality,\nusing as input a distance matrix of dimensions n × n. When n is\nlarge, classical algorithms suffer from computational problems\nand MDS configuration can not be obtained. With this package,\nwe address these problems by means of six algorithms, being two\nof them original proposals: - Landmark MDS proposed by De Silva\nV. and JB. Tenenbaum (2004). - Interpolation MDS proposed by\nDelicado P. and C. Pachón-García (2021) \n(original proposal). - Reduced MDS proposed by Paradis E\n(2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) -\nDivide-and-conquer MDS proposed by Delicado P. and C.\nPachón-García (2021) (original proposal). -\nFast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang\n(2006).", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.2.3", "URL": "https://github.com/pachoning/bigmds", "BugReports": "https://github.com/pachoning/bigmds/issues", "Repository": "https://pachoning.r-universe.dev", "Date/Publication": "2024-01-09 14:51:17 UTC", "RemoteUrl": "https://github.com/pachoning/bigmds", "RemoteRef": "HEAD", "RemoteSha": "26c5739325d0ada177e4a0e216bf599b70f49ce7", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 07:35:09 UTC", "User": "root" }, "Author": "Cristian Pachón García [aut, cre] (ORCID:\n),\nPedro Delicado [aut] (ORCID: )", "Maintainer": "Cristian Pachón García ", "MD5sum": "063b9678afdefdcc9c5a8bd47f26cbd8", "_user": "pachoning", "_type": "src", "_file": "bigmds_3.0.0.tar.gz", "_fileid": "e07cd1e2c034808bc6e351d8d54ada563a4fb68eb9cd918ccdf7a50268d0a8ef", "_filesize": 179408, "_sha256": "e07cd1e2c034808bc6e351d8d54ada563a4fb68eb9cd918ccdf7a50268d0a8ef", "_created": 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