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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 default method is a\nphylogenetic baseline calibrated on held-out data; an optional\ngraph neural network correction, which is needed to use\ncovariates, is blended in through a per-trait gate that retains\na baseline fallback. Provides conformal prediction intervals\nfor continuous, count, ordinal, and proportion traits and an\nexperimental analysis-aware multiple-imputation workflow for\none missing continuous covariate in Gaussian linear,\nbinomial-logit, and Gaussian random-intercept models, with\nRubin pooling limited to fixed effects. Stochastic\ngraph-network and posterior-tree completions are prediction\ndiagnostics rather than validated inferential imputations.\nBundled datasets include 300-species and 9,993-species\nbird-trait subsets with matching example phylogenetic trees.\nRubin (1987, ISBN: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>.",
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    "read_traits",
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      "table": false,
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      "table": false,
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      "title": "Full AVONET morphological and ecological trait data for 9,993 bird species",
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    {
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      "title": "AVONET morphological and ecological trait data for 300 bird species",
      "topics": [
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    },
    {
      "page": "build_phylo_graph",
      "title": "Build a phylogenetic graph representation from a tree",
      "topics": [
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    {
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      "topics": [
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    },
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      "topics": [
        "confusion_matrix"
      ]
    },
    {
      "page": "cross_validate",
      "title": "k-fold cross-validation for pigauto trait imputation",
      "topics": [
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    {
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      "title": "Evaluate a fitted pigauto model on its test set",
      "topics": [
        "evaluate"
      ]
    },
    {
      "page": "evaluate_imputation",
      "title": "Evaluate imputation performance against known values",
      "topics": [
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      ]
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      "page": "fit_baseline",
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      "topics": [
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      ]
    },
    {
      "page": "fit_pigauto",
      "title": "Fit a pigauto model for trait imputation",
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    },
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      "title": "Impute missing phylogenetic traits (convenience wrapper)",
      "topics": [
        "impute"
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    },
    {
      "page": "load_pigauto",
      "title": "Load a saved pigauto model",
      "topics": [
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    },
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      "title": "Split cells into train/val/test for imputation evaluation",
      "topics": [
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      "page": "mask_missing",
      "title": "Create an observed/missing mask matrix",
      "topics": [
        "mask_missing"
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      "title": "Generate experimental stochastic completion datasets",
      "topics": [
        "multi_impute"
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      "title": "Analysis-aware multiple imputation for narrow regression models",
      "topics": [
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      "title": "Posterior-tree prediction sensitivity",
      "topics": [
        "multi_impute_trees"
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    {
      "page": "pigauto_report",
      "title": "Generate an HTML imputation report from a pigauto fit",
      "topics": [
        "pigauto_report"
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    },
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      "page": "plot_comparison",
      "title": "Forest-plot style comparison of benchmark results",
      "topics": [
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      "title": "Plot training history (ggplot2, deprecated)",
      "topics": [
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    },
    {
      "page": "plot_uncertainty",
      "title": "Plot uncertainty ribbons for imputed trait values",
      "topics": [
        "plot_uncertainty"
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    },
    {
      "page": "plot.pigauto_benchmark",
      "title": "Plot a pigauto benchmark",
      "topics": [
        "plot.pigauto_benchmark"
      ]
    },
    {
      "page": "plot.pigauto_fit",
      "title": "Plot diagnostics for a fitted pigauto model",
      "topics": [
        "plot.pigauto_fit"
      ]
    },
    {
      "page": "plot.pigauto_pred",
      "title": "Plot predictions from a pigauto model",
      "topics": [
        "plot.pigauto_pred"
      ]
    },
    {
      "page": "plot.pigauto_result",
      "title": "Plot diagnostics for a pigauto result",
      "topics": [
        "plot.pigauto_result"
      ]
    },
    {
      "page": "pool_mi",
      "title": "Pool downstream model fits across multiple imputations (Rubin's rules)",
      "topics": [
        "pool_mi"
      ]
    },
    {
      "page": "predict.pigauto_fit",
      "title": "Impute missing traits using a fitted pigauto model",
      "topics": [
        "predict.pigauto_fit"
      ]
    },
    {
      "page": "preprocess_traits",
      "title": "Preprocess trait data: align to tree, encode into latent space",
      "topics": [
        "preprocess_traits"
      ]
    },
    {
      "page": "print.pigauto_check",
      "title": "Print a pigauto input check",
      "topics": [
        "print.pigauto_check"
      ]
    },
    {
      "page": "print.summary_pigauto_result",
      "title": "Print a pigauto result summary",
      "topics": [
        "print.summary_pigauto_result"
      ]
    },
    {
      "page": "pull_gbif_centroids",
      "title": "Fetch species range-centroid covariates from GBIF",
      "topics": [
        "pull_gbif_centroids"
      ]
    },
    {
      "page": "pull_worldclim_per_species",
      "title": "Fetch per-species bioclim covariates from WorldClim v2.1",
      "topics": [
        "pull_worldclim_per_species"
      ]
    },
    {
      "page": "read_traits",
      "title": "Read trait data from a CSV file or data frame",
      "topics": [
        "read_traits"
      ]
    },
    {
      "page": "read_tree",
      "title": "Read a phylogenetic tree from a file",
      "topics": [
        "read_tree"
      ]
    },
    {
      "page": "save_pigauto",
      "title": "Save a fitted pigauto model",
      "topics": [
        "save_pigauto"
      ]
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