{
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  "Package": "POEMED",
  "Type": "Package",
  "Title": "Power Enhancement for High-Dimensional Mediation Analysis",
  "Version": "1.0.0",
  "Date": "2026-10-05",
  "Authors@R": "c(\nperson(\"Xiufan\", \"Yu\", role = \"aut\",\nemail = \"xiufan.yu@nd.edu\"),\nperson(\"Ken\", \"Kelley\", role = c(\"aut\", \"cre\"),\nemail = \"kkelley@nd.edu\",\ncomment = c(ORCID = \"0000-0002-4756-8360\")))",
  "Description": "Provides powerful global tests for high-dimensional\nmediation analysis with heterogeneous (and even contrasting)\nmediation effects, for continuous, binary, and count outcomes.\nThe power enhancement (PE) idea augments a Wald test on the\ntotal indirect effect with a component that captures marginal\nsignals from individual mediators, so that active mediators are\ndetected even when their individual indirect effects cancel and\nthe total indirect effect is zero. The package also identifies\nwhich individual mediators are active with familywise error\nrate or false discovery rate control. The methods implement Yu\nand Kelley (in press), \"Power Enhancement in High-Dimensional\nHeterogeneous Mediation Analysis\" (Journal of the American\nStatistical Association); the package also simulates mediation\ndata under the homogeneous, heterogeneous, and contrasting\npatterns that the article studies, ships its empirical\nhealth-expenditure data as a benchmark and reruns that\nempirical analysis, and runs the designs of its Monte Carlo\nsize and power studies. The name, 'POEMED', from POwer-Enhanced\nMEDiation, encodes the method. Every estimation and testing\nfunction returns a tidy 'data.frame' that prints with sensible\nrounding and composes with the broader R ecosystem.",
  "License": "GPL (>= 3)",
  "Copyright": "Xiufan Yu and Ken Kelley, except the WHO_health_mediation\nand WHO_indicator_codebook data sets, which redistribute World\nHealth Organization (Global Health Expenditure Database) and\nWorld Bank (World Development Indicators) data under the terms\nstated on the help page of WHO_health_mediation and, in the\nsource package, in the file LICENSE.note.",
  "URL": "https://github.com/yelleKneK/POEMED,\nhttps://yelleknek.github.io/POEMED/",
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  "Packaged": {
    "Date": "2026-10-18 18:23:46 UTC",
    "User": "root"
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  "Author": "Xiufan Yu [aut], Ken Kelley [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-4756-8360>)",
  "Maintainer": "Ken Kelley <kkelley@nd.edu>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-10-18 14:43:17 UTC",
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  "_created": "2026-10-18T18:23:46.000Z",
  "_published": "2026-10-18T18:28:41.373Z",
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    "pe_mediation",
    "pe_mediation_linear",
    "pe_mediation_logistic",
    "pe_mediation_poisson",
    "pe_mediators",
    "pe_power_curve",
    "pe_selection",
    "pe_simulation_study",
    "simulate_guo_mediation",
    "simulate_mediation_data",
    "ss_power_pe_mediation",
    "tidy",
    "WHO_mediation_analysis",
    "WHO_mediation_design"
  ],
  "_datasets": [
    {
      "name": "example_binary",
      "title": "Example High-Dimensional Mediation Data Sets",
      "object": "example_binary",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "example_continuous",
      "title": "Example High-Dimensional Mediation Data Sets",
      "object": "example_continuous",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "example_count",
      "title": "Example High-Dimensional Mediation Data Sets",
      "object": "example_count",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
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      "title": "Calibration Constants for the Real-Data-Motivated Heterogeneous Setting",
      "object": "guo_calibration",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "title": "WHO / World Bank Health-Expenditure Mediation Data (Benchmark Data Set)",
      "object": "WHO_health_mediation",
      "class": [
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      ],
      "fields": [
        "code",
        "country",
        "region",
        "income",
        "year",
        "gdp_growth",
        "imr",
        "u5mr",
        "leb",
        "lbw",
        "pou",
        "che_gdp",
        "che_pc_usd",
        "che",
        "gghed",
        "pvtd",
        "ext",
        "dom_che",
        "gghed_che",
        "pvtd_che",
        "oops_che",
        "vpp_che",
        "ext_che",
        "gghed_gdp",
        "gghed_gge",
        "gghed_pc_usd",
        "pvtd_pc_usd",
        "oop_pc_usd",
        "ext_pc_usd",
        "cfa_che",
        "gfa_che",
        "chi_che",
        "shi_che",
        "chi_pvt_che",
        "vfa_che",
        "vhi_che",
        "gge_gdp",
        "gdp_pc_usd",
        "che_usd",
        "gghed_usd",
        "pvtd_usd",
        "ext_usd",
        "che_ncu_pc",
        "gghed_ncu_pc",
        "pvtd_ncu_pc",
        "ext_ncu_pc",
        "che_ppp_pc",
        "gghed_ppp_pc",
        "pvtd_ppp_pc",
        "ext_ppp_pc",
        "pvtd_gdp",
        "ext_gdp",
        "che_ncu2021",
        "gghed_ncu2021",
        "pvtd_ncu2021",
        "ext_ncu2021",
        "che_usd2021",
        "gghed_usd2021",
        "pvtd_usd2021",
        "ext_usd2021",
        "che_ncu2021_pc",
        "gghed_ncu2021_pc",
        "pvtd_ncu2021_pc",
        "ext_ncu2021_pc",
        "che_usd2021_pc",
        "gghed_usd2021_pc",
        "pvtd_usd2021_pc",
        "ext_usd2021_pc"
      ],
      "rows": 2002,
      "table": true,
      "tojson": true
    },
    {
      "name": "WHO_indicator_codebook",
      "title": "Codebook for the WHO Health-Expenditure Indicators",
      "object": "WHO_indicator_codebook",
      "class": [
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      ],
      "fields": [
        "indicator",
        "description"
      ],
      "rows": 57,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "example_data",
      "title": "Example High-Dimensional Mediation Data Sets",
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        "example_continuous",
        "example_count",
        "example_data"
      ]
    },
    {
      "page": "guo_calibration",
      "title": "Calibration Constants for the Real-Data-Motivated Heterogeneous Setting",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "guo_calibration"
      ]
    },
    {
      "page": "mediation_ar1_cov",
      "title": "Autoregressive (AR(1)) Covariance Matrix for Mediator Simulation",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "mediation_ar1_cov"
      ]
    },
    {
      "page": "pe_identification_study",
      "title": "Monte Carlo Study of Individual-Mediator Identification (FWER / FDR)",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "pe_identification_study"
      ]
    },
    {
      "page": "pe_mediate",
      "title": "Power-Enhanced Mediation Test From a Data Frame (Formula Interface)",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "pe_mediate"
      ]
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      "page": "pe_mediation",
      "title": "Power-Enhanced Test for High-Dimensional Mediation",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "pe_mediation"
      ]
    },
    {
      "page": "pe_mediation_linear",
      "title": "Power-Enhanced Mediation Test for a Continuous Outcome",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "pe_mediation_linear"
      ]
    },
    {
      "page": "pe_mediation_logistic",
      "title": "Power-Enhanced Mediation Test for a Binary Outcome",
      "concept": [
        "mediation tests"
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      "topics": [
        "pe_mediation_logistic"
      ]
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    {
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      "title": "Power-Enhanced Mediation Test for a Count Outcome",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "pe_mediation_poisson"
      ]
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      "concept": [
        "mediation tests"
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      "topics": [
        "pe_mediators"
      ]
    },
    {
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      "title": "Monte Carlo Size and Power Curve for the PE Mediation Tests",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "pe_power_curve"
      ]
    },
    {
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      "title": "Active Mediators Identified Under Each Multiplicity Method",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "pe_selection"
      ]
    },
    {
      "page": "pe_simulation_study",
      "title": "Run the Power Study Under Several Mediation Patterns at Once",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "pe_simulation_study"
      ]
    },
    {
      "page": "plot.poemed_tbl",
      "title": "Plot a POEMED Result",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "plot.poemed_tbl"
      ]
    },
    {
      "page": "poemed_broom",
      "title": "Broom Verbs for POEMED Results",
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        "glance.poemed_tbl",
        "poemed_broom",
        "tidy",
        "tidy.poemed_tbl"
      ]
    },
    {
      "page": "poemed_tbl",
      "title": "Tidy Printing for POEMED Result Tables",
      "topics": [
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        "poemed_tbl",
        "print.poemed_tbl",
        "rbind.poemed_tbl",
        "[.poemed_tbl"
      ]
    },
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      "page": "simulate_guo_mediation",
      "title": "Simulate the Real-Data-Motivated Heterogeneous Mediation Setting",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "simulate_guo_mediation"
      ]
    },
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      "title": "Simulate High-Dimensional Mediation Data",
      "concept": [
        "mediation simulation"
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      "topics": [
        "simulate_mediation_data"
      ]
    },
    {
      "page": "ss_power_pe_mediation",
      "title": "Simulation-Based Sample Size Planning for the Power-Enhanced Mediation Test",
      "concept": [
        "mediation simulation"
      ],
      "topics": [
        "ss_power_pe_mediation"
      ]
    },
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      "page": "summary.poemed_tbl",
      "title": "Summarize a POEMED Table",
      "concept": [
        "mediation tests"
      ],
      "topics": [
        "print.summary.poemed_tbl",
        "summary.poemed_tbl"
      ]
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      "title": "WHO / World Bank Health-Expenditure Mediation Data (Benchmark Data Set)",
      "concept": [
        "WHO mediation data"
      ],
      "topics": [
        "WHO_health_mediation"
      ]
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      "concept": [
        "WHO mediation data"
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        "WHO mediation data"
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        "WHO_mediation_analysis"
      ]
    },
    {
      "page": "WHO_mediation_design",
      "title": "Build a Mediation Design From the WHO Health-Expenditure Data",
      "concept": [
        "WHO mediation data"
      ],
      "topics": [
        "WHO_mediation_design"
      ]
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        "5. Reading the Result Table",
        "6. Identifying Which Mediators Are Active",
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        "9. The Real Application: Health Spending as a Mediator",
        "Building One Analysis Design",
        "The Full Subgroup Analysis",
        "Why the Preprocessing Matters",
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        "A Formula Interface for Data Frames",
        "Why Each Mediator? The Per-Mediator Table",
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        "All Three Multiplicity Methods at Once",
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