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).


Here is the website for the package. Monte Carlo simulations are computer experiments designed to study the performance of statistical methods under known data-generating conditions (Morris, White, & Crowther, 2019). Methodologists use simulations to examine questions such as: (1) how does ordinary least squares regression perform if errors are heteroskedastic? (2) how does the presence of missing data affect treatment effect estimates from a propensity score analysis? (3) how does cluster robust variance estimation perform when the number of clusters is small?