{
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  "Title": "Prediction Explanation with Dependence-Aware Shapley Values",
  "Description": "Complex machine learning models are often hard to\ninterpret. However, in many situations it is crucial to\nunderstand and explain why a model made a specific prediction.\nShapley values is the only method for such prediction\nexplanation framework with a solid theoretical foundation.\nPreviously known methods for estimating the Shapley values do,\nhowever, assume feature independence. This package implements\nmethods which accounts for any feature dependence, and thereby\nproduces more accurate estimates of the true Shapley values. An\naccompanying 'Python' wrapper ('shaprpy') is available through\nPyPI.",
  "Authors@R": "c(\nperson(\"Martin\", \"Jullum\", email = \"Martin.Jullum@nr.no\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0003-3908-5155\")),\nperson(\"Lars Henry Berge\", \"Olsen\", email = \"lhbolsen@nr.no\", role = \"aut\", comment = c(ORCID = \"0009-0006-9360-6993\")),\nperson(\"Annabelle\", \"Redelmeier\", email = \"ardelmeier@gmail.com\", role = \"aut\"),\nperson(\"Jon\", \"Lachmann\", email = \"Jon@lachmann.nu\", role = \"aut\", comment = c(ORCID = \"0000-0001-8396-5673\")),\nperson(\"Nikolai\", \"Sellereite\", email = \"nikolaisellereite@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-4671-0337\")),\nperson(\"Anders\", \"Løland\", email = \"Anders.Loland@nr.no\", role = \"ctb\"),\nperson(\"Jens Christian\", \"Wahl\", email = \"jens.c.wahl@gmail.com\", role = \"ctb\"),\nperson(\"Camilla\", \"Lingjærde\", role = \"ctb\"),\nperson(\"Norsk Regnesentral\", role =  c(\"cph\", \"fnd\"))\n)",
  "URL": "https://norskregnesentral.github.io/shapr/,\nhttps://github.com/NorskRegnesentral/shapr/",
  "BugReports": "https://github.com/NorskRegnesentral/shapr/issues",
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  "Repository": "https://norskregnesentral.r-universe.dev",
  "Date/Publication": "2026-06-09 12:38:26 UTC",
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  "Packaged": {
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  "Author": "Martin Jullum [cre, aut] (ORCID:\n<https://orcid.org/0000-0003-3908-5155>),\nLars Henry Berge Olsen [aut] (ORCID:\n<https://orcid.org/0009-0006-9360-6993>),\nAnnabelle Redelmeier [aut],\nJon Lachmann [aut] (ORCID: <https://orcid.org/0000-0001-8396-5673>),\nNikolai Sellereite [aut] (ORCID:\n<https://orcid.org/0000-0002-4671-0337>),\nAnders Løland [ctb],\nJens Christian Wahl [ctb],\nCamilla Lingjærde [ctb],\nNorsk Regnesentral [cph, fnd]",
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    "append_vS_list",
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    "cli_compute_vS",
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    "cli_startup",
    "coalition_matrix_cpp",
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    "plot_vaeac_eval_crit",
    "plot_vaeac_imputed_ggpairs",
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    "prepare_data",
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    "prepare_next_iteration",
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      "title": "Explain the Output of Machine Learning Models with Dependence-Aware (Conditional/Observational) Shapley Values",
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      "title": "Explain a Forecast from Time Series Models with Dependence-Aware (Conditional/Observational) Shapley Values",
      "topics": [
        "explain_forecast"
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      "page": "get_extra_comp_args_default",
      "title": "Get the Default Values for the Extra Computation Arguments",
      "topics": [
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    },
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      "page": "get_iterative_args_default",
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      "title": "Get the Default Values for the Output Arguments",
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      "title": "Extract Components from a Shapr Object",
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      "title": "Provide a 'data.table' with the Supported Models",
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      "page": "plot_MSEv_eval_crit",
      "title": "Plots of the MSEv Evaluation Criterion",
      "topics": [
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      "title": "Plot of the Shapley Value Explanations",
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      "page": "print.shapr",
      "title": "Print Method for Shapr Objects",
      "topics": [
        "print.shapr"
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      "title": "Print Method for summary.shapr Objects",
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