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  "Date": "2024-05-15",
  "Authors@R": "c(person(\"Dave\", \"Armstrong\", email=\"davearmstrong.ps@gmail.com\", \nrole=c(\"aut\", \"cre\")),\nperson(\"William\", \"Jacoby\", email=\"wm.g.jacoby@gmail.com\",\nrole=c(\"aut\")))",
  "Description": "Optimal scaling of a data vector, relative to a set of\ntargets, is obtained through a least-squares transformation\nsubject to appropriate measurement constraints. The targets are\nusually predicted values from a statistical model. If the data\nare nominal level, then the transformation must be\nidentity-preserving. If the data are ordinal level, then the\ntransformation must be monotonic. If the data are discrete,\nthen tied data values must remain tied in the optimal\ntransformation. If the data are continuous, then tied data\nvalues can be untied in the optimal transformation.",
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    "Date": "2026-06-02 06:50:18 UTC",
    "User": "root"
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  "Author": "Dave Armstrong [aut, cre], William Jacoby [aut]",
  "Maintainer": "Dave Armstrong <davearmstrong.ps@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2024-05-17 02:38:22 UTC",
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      "title": "Optimal Scaling of a Data Vector",
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        "optiscale"
      ]
    },
    {
      "page": "elec92",
      "title": "Public Opinion During the 1992 U.S. Presidential Election",
      "topics": [
        "elec92"
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    },
    {
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      "title": "S3 methods for opscale",
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        "plot.opscale",
        "print.opscale",
        "print.summary.opscale",
        "summary.opscale"
      ]
    },
    {
      "page": "opscale",
      "title": "Optimal scaling of a data vector",
      "topics": [
        "opscale"
      ]
    },
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      "title": "Stress coefficients for 'opscale'",
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        "stress"
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