Here is the website for the package. Typical methods to conduct meta-analysis—pooling effect sizes or analyzing moderating effects with meta-regression—work under the assumption that the effect size estimates are independent. However, primary studies often report multiple estimates of effect sizes. Presence of multiple effect sizes leads to dependence as the estimates within each study are likely correlated (e.g., because the same participants provide multiple outcome scores). The increasingly popular method to handle such dependence, robust variance estimation (RVE), results in inflated Type 1 error rate when the number of studies is small (Hedges, Tipton & Johnson, 2010; Tipton, 2015).

Cluster wild bootstrapping to handle dependent effect sizes in meta-analysis with a small number of studies

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include …