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  "Title": "Multidimensional Scaling for Big Data",
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  "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) <arXiv:2007.11919>\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) <arXiv:2007.11919> (original proposal). -\nFast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang\n(2006).",
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  "URL": "https://github.com/pachoning/bigmds",
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  "Repository": "https://pachoning.r-universe.dev",
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