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  "Title": "Distance-Based Learning for Mixed-Type Data",
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  "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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      "fileid": "f8ce87c437bf558a5056b3acd50175fdbb563b1476130c82f86d7d5d42a56406",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/alfonsoiodicede/actions/runs/27262787937"
    }
  ]
}