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  "Package": "PoolTestR",
  "Title": "Prevalence and Regression for Pool-Tested (Group-Tested) Data",
  "Version": "0.2.1",
  "Authors@R": "c(\nperson(\"Angus\", \"McLure\", , \"angus.mclure@anu.edu.au\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2551-3059\")),\nperson(\"Caitlin\", \"Cherryh\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-6146-4376\"))\n)",
  "Description": "An easy-to-use tool for working with presence/absence\ntests on 'pooled' or 'grouped' samples. The primary application\nis for estimating prevalence of a marker in a population based\non the results of tests on pooled specimens.  This sampling\nmethod is often employed in surveillance of rare conditions in\nhumans or animals (e.g. molecular xenomonitoring). The package\nwas initially conceived as an R-based alternative to the\nmolecular xenomonitoring software, 'PoolScreen'\n<https://sites.uab.edu/statgenetics/software/>. However, it\ngoes further, allowing for estimates of prevalence to be\nadjusted for hierarchical sampling frames, and perform flexible\nmixed-effect regression analyses (McLure et al. Environmental\nModelling and Software.  <DOI:10.1016/j.envsoft.2021.105158>).\nThe package is currently in early stages, however more features\nare planned or in the works: e.g. adjustments for imperfect\ntest specificity/sensitivity, functions for helping with\noptimal experimental design, and functions for spatial\nmodelling.",
  "License": "GPL (>= 3)",
  "URL": "https://github.com/AngusMcLure/PoolTestR",
  "BugReports": "https://github.com/AngusMcLure/PoolTestR/issues",
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  "Repository": "https://angusmclure.r-universe.dev",
  "Date/Publication": "2025-11-25 08:25:17 UTC",
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  "Author": "Angus McLure [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-2551-3059>),\nCaitlin Cherryh [ctb] (ORCID: <https://orcid.org/0000-0001-6146-4376>)",
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        "Result"
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        "Village",
        "Region",
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      "topics": [
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      ]
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      "title": "Predicting Prevalence from a Mixed or Fixed Effect Logistic Regression with Presence/Absence Tests on Pooled Samples",
      "topics": [
        "getPrevalence",
        "getPrevalence.brmsfit",
        "getPrevalence.glm",
        "getPrevalence.glmerMod"
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      "title": "Estimation of prevalence based on presence/absence tests on pooled samples in a hierarchical sampling frame. Uses an intercept-only random effects model to model prevalence at population level. See PoolReg and PoolRegBayes for full mixed-effect modelling",
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