{
  "_id": "6a102e7dacfb0bcc41c94492",
  "Package": "MetaHunt",
  "Title": "Privacy-Preserving Meta-Analysis via Low-Rank Basis Hunting",
  "Version": "0.1.0",
  "Authors@R": "c(\nperson(\"Wenqi\", \"Shi\", , \"wenqishi18@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Kosuke\", \"Imai\", , \"imai@harvard.edu\", role = \"aut\"),\nperson(\"Yi\", \"Zhang\", , \"yizhang0017@gmail.com\", role = \"aut\"))",
  "Description": "Tools for privacy-preserving meta-analysis of\nfunction-valued quantities across heterogeneous studies.\nImplements the 'MetaHunt' pipeline, including the denoised\nfunctional Successive Projection Algorithm (d-fSPA) for basis\nhunting, constrained weight estimation, Dirichlet regression of\nweights on study-level covariates, target prediction, and\nsplit/cross conformal prediction intervals. Operates on\naggregate-level function evaluations, so individual-level data\nfrom source studies are not required. Methodology described in\nShi, Imai, and Zhang (2026) <doi:10.48550/arXiv.2604.23847>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.3",
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  "URL": "https://github.com/WShi18/MetaHunt,\nhttps://wshi18.github.io/MetaHunt/,\nhttps://arxiv.org/abs/2604.23847",
  "BugReports": "https://github.com/WShi18/MetaHunt/issues",
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  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Author": "Wenqi Shi [aut, cre], Kosuke Imai [aut], Yi Zhang [aut]",
  "Maintainer": "Wenqi Shi <wenqishi18@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-05-12 21:44:49 UTC",
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    "conformal_from_fit",
    "coverage",
    "cross_conformal",
    "cv_error_curve",
    "dfspa",
    "f_hat_from_models",
    "fit_weight_model",
    "metahunt",
    "minmax_regret",
    "predict_target",
    "project_to_simplex",
    "reconstruction_error_curve",
    "select_denoising_params",
    "split_conformal"
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    {
      "page": "apply_wrapper",
      "title": "Reduce predicted functions to scalars via a user-supplied wrapper",
      "topics": [
        "apply_wrapper"
      ]
    },
    {
      "page": "build_grid",
      "title": "Build a shared evaluation grid from a reference dataset",
      "topics": [
        "build_grid"
      ]
    },
    {
      "page": "coef.metahunt_weight_model",
      "title": "Extract coefficients from a MetaHunt weight model",
      "topics": [
        "coef.metahunt_weight_model"
      ]
    },
    {
      "page": "conformal_from_fit",
      "title": "Split conformal intervals from a pre-fit MetaHunt pipeline",
      "topics": [
        "conformal_from_fit"
      ]
    },
    {
      "page": "coverage",
      "title": "Empirical coverage of a conformal prediction-interval object",
      "topics": [
        "coverage"
      ]
    },
    {
      "page": "cross_conformal",
      "title": "Cross-conformal prediction intervals (pooled K-fold scores)",
      "topics": [
        "cross_conformal"
      ]
    },
    {
      "page": "cv_error_curve",
      "title": "Cross-validated prediction-error curve for basis-rank selection",
      "topics": [
        "cv_error_curve"
      ]
    },
    {
      "page": "dfspa",
      "title": "Denoised functional Successive Projection Algorithm (d-fSPA)",
      "topics": [
        "dfspa"
      ]
    },
    {
      "page": "f_hat_from_models",
      "title": "Build the 'F_hat' matrix from a list of fitted study-level models",
      "topics": [
        "f_hat_from_models"
      ]
    },
    {
      "page": "fit_weight_model",
      "title": "Fit a weight model mapping study-level covariates to simplex weights",
      "topics": [
        "fit_weight_model"
      ]
    },
    {
      "page": "metahunt",
      "title": "Fit the full MetaHunt pipeline",
      "topics": [
        "metahunt"
      ]
    },
    {
      "page": "minmax_regret",
      "title": "Minimax-regret aggregator for multisite function-valued estimands",
      "topics": [
        "minmax_regret"
      ]
    },
    {
      "page": "plot.metahunt",
      "title": "Plot recovered basis functions from a MetaHunt fit",
      "topics": [
        "plot.metahunt"
      ]
    },
    {
      "page": "plot.metahunt_conformal",
      "title": "Plot a conformal prediction-interval object",
      "topics": [
        "plot.metahunt_conformal"
      ]
    },
    {
      "page": "predict_target",
      "title": "Predict the target function for new study-level covariates",
      "topics": [
        "predict_target"
      ]
    },
    {
      "page": "predict.metahunt",
      "title": "Predict target functions (or scalar summaries) from a MetaHunt fit",
      "topics": [
        "predict.metahunt"
      ]
    },
    {
      "page": "predict.metahunt_weight_model",
      "title": "Predict simplex weights for new study-level covariates",
      "topics": [
        "predict.metahunt_weight_model"
      ]
    },
    {
      "page": "print.metahunt_denoising_search",
      "title": "Print method for d-fSPA denoising parameter search results",
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      ]
    },
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      "title": "Print a 'summary.metahunt' object",
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        "print.summary.metahunt"
      ]
    },
    {
      "page": "project_to_simplex",
      "title": "Project study-level functions onto the simplex spanned by basis functions",
      "topics": [
        "project_to_simplex"
      ]
    },
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      "title": "Reconstruction-error curve for basis-rank selection",
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        "reconstruction_error_curve"
      ]
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      "title": "Choose d-fSPA denoising parameters by cross-validation",
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      ]
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      "page": "summary.metahunt",
      "title": "Summarise a MetaHunt fit",
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        "summary.metahunt"
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      "title": "Summarise a conformal prediction-interval object",
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      ]
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        "The wrapper protocol",
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