{ "_id": "6a253a0f4b233be19839857e", "Package": "MAIVE", "Type": "Package", "Title": "Meta Analysis Instrumental Variable Estimator", "Encoding": "UTF-8", "Version": "0.0.2.11", "RoxygenNote": "7.3.2", "Authors@R": "c(\nperson(given = \"Zuzana\", family = \"Irsova\",\nrole = \"aut\",\ncomment = c(affiliation = \"Charles University, Prague\")),\nperson(given = \"Pedro R. D.\", family = \"Bom\",\nrole = \"aut\",\ncomment = c(affiliation = \"University of Deusto, Bilbao\")),\nperson(given = \"Tomas\", family = \"Havranek\",\nrole = \"aut\",\ncomment = c(affiliation = c(\"Charles University, Prague\",\n\"Centre for Economic Policy Research, London\",\n\"Meta-Research Innovation Center, Stanford\"))),\nperson(given = \"Heiko\", family = \"Rachinger\",\nemail = \"heiko.rachinger@uib.es\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(affiliation = \"University of the Balearic Islands, Palma\"))\n)", "Description": "Meta-analysis traditionally assigns more weight to studies\nwith lower standard errors, assuming higher precision. However,\nin observational research, precision must be estimated and is\nvulnerable to manipulation, such as p-hacking, to achieve\nstatistical significance. This can lead to spurious precision,\ninvalidating inverse-variance weighting and bias-correction\nmethods like funnel plots. Common methods for addressing\npublication bias, including selection models, often fail or\nexacerbate the problem. This package introduces an instrumental\nvariable approach to limit bias caused by spurious precision in\nmeta-analysis.", "License": "MIT + file LICENSE", "VignetteBuilder": "knitr", "Repository": "https://meta-analysis-es.r-universe.dev", "Date/Publication": "2025-10-13 15:25:44 UTC", "RemoteUrl": "https://github.com/meta-analysis-es/maive", "RemoteRef": "HEAD", "RemoteSha": "60956c4a5a96655190ae92384cebfc2b055c03b6", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-07 09:26:57 UTC", "User": "root" }, "Author": "Zuzana Irsova [aut] (affiliation: Charles University, Prague),\nPedro R. D. 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