Effect size indices are useful parameters that quantify the strength of association and are unaffected by sample size. There are many available effect size parameters and estimators, but it is difficult to compare effect sizes across studies as most are defined for a specific type of population parameter. We recently introduced a new Robust Effect Size Index (RESI) and confidence interval, which is advantageous because it is not model-specific. Here we present the RESI R package, which makes it easy to report the RESI and its confidence interval for many different model classes, with a consistent interpretation across parameters and model types. The package produces coefficient, ANOVA tables, and overall Wald tests for model inputs, appending the RESI estimate and confidence interval to each. The package also includes functions for visualization and conversions to and from other effect size measures. For illustration, we analyze and interpret three different model types.

翻译：效应量指标是衡量关联强度的有用参数，且不受样本量影响。现有多种效应量参数及其估计量，但由于多数针对特定类型的总体参数定义，不同研究间的效应量难以比较。我们近期提出了一种新的稳健效应量指数（RESI）及其置信区间，其优势在于不依赖特定模型。本文介绍RESI的R语言工具包，该工具包可简便地为多种模型类别报告RESI及其置信区间，并在不同参数和模型类型间保持解释一致性。该工具包为模型输入生成系数表、方差分析表及整体Wald检验，并在各项后附加RESI估计值及置信区间。此外，工具包还包含可视化功能及与其他效应量度量之间的转换函数。为进行说明，我们分析并解读了三种不同模型类型。