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  "Title": "Positive and Unlabeled Learning from Unbalanced Cases and Sparse\nStructures",
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  "Description": "Provides PLUS-derived extensions for positive and\nunlabeled (PU) learning from unbalanced cases and sparse\nstructures, based on Zhou et al. (2022)\n<doi:10.1371/journal.pcbi.1009956>. Iteratively relabels\nunlabeled observations via penalised logistic regression and\npseudo-label updates, then refits a final sparse model.\nIncludes weighted bootstrap sampling, convergence diagnostics,\nprediction, coefficient extraction, and assessment utilities.",
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