{
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  "Title": "Fill in Missing Species Traits Using a Phylogenetic Tree",
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  "Authors@R": "person(\"Shinichi\", \"Nakagawa\", role = c(\"aut\", \"cre\", \"cph\"), email = \"itchyshin@gmail.com\")",
  "Description": "Imputes missing species trait data for comparative\nanalyses by combining three sources of information:\nphylogenetic similarity (closely related species share similar\ntraits), cross-trait correlations (observed traits inform\nmissing ones), and optional environmental covariates (climate,\nhabitat, geography). Handles continuous measurements, counts,\nbinary variables, ordered categories, unordered categories,\nbounded proportions, zero-inflated counts, and compositional\nmulti-proportion data in a single call. The method blends a\nphylogenetic baseline with a graph neural network correction; a\nper-trait gate calibrated on held-out data ensures the network\nonly contributes when it improves on the baseline. Provides\nconformal prediction intervals for continuous, count, and\nordinal traits and an experimental analysis-aware\nmultiple-imputation workflow for one missing continuous\ncovariate in Gaussian linear, binomial-logit, and Gaussian\nrandom-intercept models, with Rubin pooling limited to fixed\neffects. Stochastic graph-network and posterior-tree\ncompletions are prediction diagnostics rather than validated\ninferential imputations. Tested up to 10,000 species. Bundled\ndatasets include 300-species and 9,993-species bird-trait\nsubsets with matching example phylogenetic trees. Rubin (1987,\nISBN:978-0-471-08705-2); Vovk et al. (2005,\nISBN:978-0-387-25061-8); Nakagawa and de Villemereuil (2019)\n<doi:10.1093/sysbio/syy089>.",
  "License": "MIT + file LICENSE",
  "URL": "https://itchyshin.github.io/pigauto/,\nhttps://github.com/itchyshin/pigauto",
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  "Date/Publication": "2026-07-21 17:23:10 UTC",
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