{
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  "Title": "Distance-Based Learning for Mixed-Type Data",
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  "Authors@R": "c(\nperson(given = \"Alfonso\",\nfamily = \"Iodice D'Enza\",\nrole = c(\"aut\", \"cre\"),\nemail = \"iodicede@unina.it\"),\nperson(given = \"Angelos\",\nfamily = \"Markos\",\nrole = \"aut\",\nemail = \"amarkos@gmail.com\"),\nperson(given = \"Michel\",\nfamily = \"van de Velden\",\nrole = \"aut\"),\nperson(given = \"Carlo\",\nfamily = \"Cavicchia\",\nrole = \"aut\"))",
  "Description": "Provides tools for constructing, computing, and using\ndistance measures for numerical, categorical, and mixed-type\ndata. The package implements a flexible framework in which\ncontinuous and categorical components can be combined under\nadditive, commensurable, and association-aware specifications.\nSupported methods include classical distances such as Gower,\nEuclidean, Manhattan, and Mahalanobis-type distances;\ncategorical dissimilarities such as simple matching,\noccurrence-frequency, and association-based measures; and\nmixed-type presets designed to reduce biases due to variable\ntype, scale, distribution, redundancy, and number of\ncategories. The package also provides scaling options,\nsupervised and unsupervised distance constructions,\nleave-one-variable-out tools for distance-based variable\nimportance, and integration with distance-based learning\nworkflows such as nearest-neighbour prediction, partitioning\naround medoids, and spectral clustering. Methods are motivated\nby van de Velden, Iodice D'Enza, Markos, and Cavicchia (2026)\n<doi:10.1080/10618600.2026.2680181> and related work on\ncategorical and mixed-type dissimilarities.",
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  "Date/Publication": "2026-10-19 11:32:35 UTC",
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  "Author": "Alfonso Iodice D'Enza [aut, cre],\nAngelos Markos [aut],\nMichel van de Velden [aut],\nCarlo Cavicchia [aut]",
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      "table": true,
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      "object": "wdi_2022",
      "class": [
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      "fields": [
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        "region",
        "income",
        "lending",
        "gdp_pc_kusd",
        "life_exp",
        "unemployment",
        "urban_pop",
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      "rows": 181,
      "table": true,
      "tojson": true
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      "title": "Create a grid of `mdist()` method specifications",
      "topics": [
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      "page": "autoplot.MDistBenchmark",
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      "topics": [
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      "topics": [
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      "title": "Compare LOVO diagnostics across multiple distance specifications",
      "topics": [
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      "title": "Congruence coefficient between two configurations",
      "topics": [
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      "title": "List available `manydist` methods",
      "topics": [
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      "topics": [
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      "topics": [
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      "topics": [
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