<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>meta-analysis-es.r-universe.dev</title><link>https://meta-analysis-es.r-universe.dev</link><description>Recent package updates in meta-analysis-es</description><generator>R-universe</generator><image><url>https://github.com/meta-analysis-es.png</url><title>R packages by meta-analysis-es</title><link>https://meta-analysis-es.r-universe.dev</link></image><lastBuildDate>Mon, 13 Oct 2025 15:25:44 GMT</lastBuildDate><item><title>[meta-analysis-es] MAIVE 0.0.2.11</title><author>heiko.rachinger@uib.es (Heiko Rachinger)</author><description>Meta-analysis traditionally assigns more weight to studies
with lower standard errors, assuming higher precision. However,
in observational research, precision must be estimated and is
vulnerable to manipulation, such as p-hacking, to achieve
statistical significance. This can lead to spurious precision,
invalidating inverse-variance weighting and bias-correction
methods like funnel plots. Common methods for addressing
publication bias, including selection models, often fail or
exacerbate the problem. This package introduces an instrumental
variable approach to limit bias caused by spurious precision in
meta-analysis.</description><link>https://github.com/r-universe/meta-analysis-es/actions/runs/27088550331</link><pubDate>Mon, 13 Oct 2025 15:25:44 GMT</pubDate><r:package>MAIVE</r:package><r:version>0.0.2.11</r:version><r:status>success</r:status><r:repository>https://meta-analysis-es.r-universe.dev</r:repository><r:upstream>https://github.com/meta-analysis-es/maive</r:upstream></item></channel></rss>