{
  "_id": "6a169c81acfb0bcc41d7e84c",
  "Package": "starling",
  "Type": "Package",
  "Title": "Link Infectious Disease Cases to Vaccination and Hospitalization\nRecords",
  "Version": "0.6.5",
  "Authors@R": "person(\"Nicolas\", \"Smoll\", email = \"nrsmoll@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6923-9701\"))",
  "Description": "Facilitates probabilistic record linkage between\ninfectious disease surveillance datasets (notifiable disease\nregisters, outbreak line-lists), vaccination registries, and\nhospitalization records using methods based on Fellegi and\nSunter (1969) <doi:10.1080/01621459.1969.10501049> and Sayers\net al. (2016) <doi:10.1093/ije/dyv322>. The package provides\ncore functions for data preparation, linkage, and analysis:\nclean_the_nest() standardizes variable names and formats across\nheterogeneous datasets; murmuration() performs machine\nlearning-based record linkage using blocking variables and\nsimilarity metrics; molting() deidentifies datasets for secure\nsharing; homing() re-identifies previously deidentified\ndatasets; plumage() identifies and categorizes comorbidities;\nand preening() creates analysis-ready variables including age\ncategories and temporal groupings. Designed for epidemiological\nresearch linking acute and post-acute disease outcomes to\nvaccination status and healthcare utilization. Supports\nmultiple linkage scenarios including case-to-vaccination,\ncase-to-hospitalization, and event-based vaccination status\ndetermination (e.g., outbreak attendees, flight passengers,\nexposure site visitors).",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
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  "RoxygenNote": "7.3.2",
  "VignetteBuilder": "knitr",
  "Language": "en-US",
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  "Packaged": {
    "Date": "2026-05-27 07:09:51 UTC",
    "User": "root"
  },
  "Author": "Nicolas Smoll [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6923-9701>)",
  "Maintainer": "Nicolas Smoll <nrsmoll@gmail.com>",
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  "Repository": "https://nrsmoll.r-universe.dev",
  "Date/Publication": "2026-01-26 16:20:08 UTC",
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  "_published": "2026-05-27T07:25:53.354Z",
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    "murmuration",
    "plumage",
    "preening",
    "tweet"
  ],
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    {
      "name": "dx_data",
      "title": "Example Diagnosis Dataset",
      "object": "dx_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "identity",
        "disease_name",
        "first_name",
        "surname",
        "date_of_birth",
        "medicare_no",
        "gender",
        "postcode",
        "indigenous_status",
        "diagnosis_date"
      ],
      "rows": 50,
      "table": true,
      "tojson": true
    },
    {
      "name": "hosp_data",
      "title": "Example Hospitalization Dataset",
      "object": "hosp_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "patient_id",
        "firstname",
        "last_name",
        "birth_date",
        "medicare_number",
        "sex",
        "zip_codes",
        "cultural_heritage",
        "icd_codes",
        "date_of_admission",
        "date_of_discharge"
      ],
      "rows": 45,
      "table": true,
      "tojson": true
    },
    {
      "name": "linelist_data",
      "title": "Example Outbreak Linelist Dataset",
      "object": "linelist_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "case_id",
        "first_name",
        "surname",
        "date_of_birth",
        "medicare_no",
        "gender",
        "postcode",
        "onset_date",
        "notification_date",
        "case_classification"
      ],
      "rows": 10,
      "table": true,
      "tojson": true
    },
    {
      "name": "manifest_data",
      "title": "Example Flight Manifest Dataset",
      "object": "manifest_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "passenger_id",
        "first_name",
        "surname",
        "date_of_birth",
        "gender",
        "seat_number",
        "flight_number",
        "flight_date"
      ],
      "rows": 10,
      "table": true,
      "tojson": true
    },
    {
      "name": "vax_data",
      "title": "Example Vaccination Dataset",
      "object": "vax_data",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "patient_id",
        "firstname",
        "last_name",
        "birth_date",
        "medicare_number",
        "gender",
        "postcode",
        "vaccine_delivered",
        "service_date"
      ],
      "rows": 53,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "clean_the_nest",
      "title": "Clean datasets and establishes common variable name nomenclature",
      "topics": [
        "clean_the_nest"
      ]
    },
    {
      "page": "dx_data",
      "title": "Example Diagnosis Dataset",
      "topics": [
        "dx_data"
      ]
    },
    {
      "page": "homing",
      "title": "Homing: Relink De-identified Data Using Lookup Table",
      "topics": [
        "homing"
      ]
    },
    {
      "page": "hosp_data",
      "title": "Example Hospitalization Dataset",
      "topics": [
        "hosp_data"
      ]
    },
    {
      "page": "linelist_data",
      "title": "Example Outbreak Linelist Dataset",
      "topics": [
        "linelist_data"
      ]
    },
    {
      "page": "manifest_data",
      "title": "Example Flight Manifest Dataset",
      "topics": [
        "manifest_data"
      ]
    },
    {
      "page": "molting",
      "title": "Molt: De-identify a Dataset with Hash-based Relinking",
      "topics": [
        "molting"
      ]
    },
    {
      "page": "murmuration",
      "title": "Links case, hospital or vaccination datasets",
      "topics": [
        "murmuration"
      ]
    },
    {
      "page": "plumage",
      "title": "Identify Chronic Conditions Using ICD-10-AM U-Codes",
      "topics": [
        "plumage"
      ]
    },
    {
      "page": "preening",
      "title": "Prettification of infectious diseases datasets",
      "topics": [
        "preening"
      ]
    },
    {
      "page": "tweet",
      "title": "Examine and summarize variables in a dataset",
      "topics": [
        "tweet"
      ]
    },
    {
      "page": "vax_data",
      "title": "Example Vaccination Dataset",
      "topics": [
        "vax_data"
      ]
    }
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      "headings": [
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        "Why starling?",
        "Core Workflow",
        "Basic Example: Linking Cases to Vaccinations",
        "Step 1: Prepare the Data",
        "Step 2: Link the Datasets",
        "Step 3: Prepare for Analysis",
        "Advanced Example: Complete Case-Hospitalization-Vaccination Linkage",
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        "Flight Manifest Example",
        "Outbreak Linelist Example",
        "Understanding Linkage Types",
        "Best Practices",
        "Blocking Variables",
        "Comparison Variables",
        "Threshold Selection",
        "Date Formatting",
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        "Too many matches",
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      "created": "2026-01-26 16:20:08",
      "modified": "2026-01-26 16:20:08",
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