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  "Title": "SPRE Statistics for Exploring Heterogeneity in Meta-Analysis",
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  "Authors@R": "c(\nperson(\"Lerato E\", \"Magosi\", email = \"lmagosi@well.ox.ac.uk\",\nrole = \"aut\",),\nperson(\"Jemma C\", \"Hopewell\", email = \"jemma.hopewell@ndph.ox.ac.uk\",\nrole = \"aut\"),\nperson(\"Martin\", \"Farrall\", email = \"mfarrall@well.ox.ac.uk\",\nrole = \"aut\"),\nperson(\"Lerato E\", \"Magosi\", email = \"magosil86@gmail.com\",\nrole = \"cre\")\n)",
  "Description": "An implementation of SPRE (standardised predicted\nrandom-effects) statistics in R to explore heterogeneity in\ngenetic association meta- analyses, as described by Magosi et\nal. (2019) <doi:10.1093/bioinformatics/btz590>. SPRE statistics\nare precision weighted residuals that indicate the direction\nand extent with which individual study-effects in a\nmeta-analysis deviate from the average genetic effect. Overly\ninfluential positive outliers have the potential to inflate\naverage genetic effects in a meta-analysis whilst negative\noutliers might lower or change the direction of effect. See the\n'getspres' website for documentation and examples\n<https://magosil86.github.io/getspres/>.",
  "License": "MIT + file LICENSE",
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    {
      "page": "getspres",
      "title": "Exploring Heterogeneity in Meta-Analysis with _SPRE_ Statistics.",
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        "getspre",
        "getspres",
        "getspres.default",
        "spre",
        "spres"
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      "title": "heartgenes214.",
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        "heartgenes214"
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    },
    {
      "page": "plotspres",
      "title": "'plotspres' generates forest plots showing _SPRE_ statistics.",
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        "plotspre",
        "plotspres",
        "plotspres.default",
        "spreforest"
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        "Two popular approaches for identifying overly influential outliers",
        "The getspres R package: A two for one bargain in outlier diagnostics!",
        "Let's take a look at some examples:",
        "Data: heartgenes214",
        "The data was sourced from:"
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