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  "Title": "Maximum Diversity Weighting",
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  "Authors@R": "c(person(\"Zonglin\", \"He\", email = \"nkhezl@gmail.com\", role = c(\"aut\")), person(\"Youyi\", \"Fong\", email = \"youyifong@gmail.com\", role = c(\"cre\")))",
  "Description": "Dimension-reduction methods aim at defining a score that\nmaximizes signal diversity. Three approaches, tree weight,\nmaximum entropy weights, and maximum variance weights are\nprovided. These methods are described in He and Fong (2019)\n<DOI:10.1002/sim.8212>.",
  "License": "GPL-2",
  "Encoding": "UTF-8",
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  "Repository": "https://youyifong.r-universe.dev",
  "Date/Publication": "2025-07-27 18:28:32 UTC",
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  "Packaged": {
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    "User": "root"
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  "Author": "Zonglin He [aut],\nYouyi Fong [cre]",
  "Maintainer": "Youyi Fong <youyifong@gmail.com>",
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    "message": "Add label.cex parameter to tree.weight function\n\nIntroduced a label.cex argument to control label size in dendrogram plots within the tree.weight function. This allows users to customize label scaling without modifying par(cex) globally.\n",
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      "title": "Weights based on GSC Tree Method",
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