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  "Description": "Conduct random forests-based meta-analysis, obtain partial\ndependence plots for metaforest and classic meta-analyses, and\ncross-validate and tune metaforest- and classic meta-analyses\nin conjunction with the caret package. A requirement of classic\nmeta-analysis is that the studies being aggregated are\nconceptually similar, and ideally, close replications. However,\nin many fields, there is substantial heterogeneity between\nstudies on the same topic. Classic meta-analysis lacks the\npower to assess more than a handful of univariate moderators.\nMetaForest, by contrast, has substantial power to explore\nheterogeneity in meta-analysis. It can identify important\nmoderators from a larger set of potential candidates (Van\nLissa, 2020). This is an appealing quality, because many\nmeta-analyses have small sample sizes. Moreover, MetaForest\nyields a measure of variable importance which can be used to\nidentify important moderators, and offers partial prediction\nplots to explore the shape of the marginal relationship between\nmoderators and effect size.",
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  "Repository": "https://cjvanlissa.r-universe.dev",
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