metaselection
Meta-analytic selection models with cluster-robust and cluster-bootstrap standard errors for dependent effect size estimates
Fits a flexible class of p-value selection models for meta-analysis and meta-regression models, providing standard errors and confidence intervals based on either cluster-robust variance estimators (i.e., sandwich estimators) or cluster-level bootstrapping to handle dependent effect size estimates. Supported models include generalizations of the step-function selection model as proposed by Vevea and Hedges (1995) and the beta-function selection model as proposed by Citkowicz and Vevea (2017).
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