Estimating sample size and statistical power is an essential part of a good study design. This R package allows users to conduct power analysis based on Monte Carlo simulations in settings in which consideration of the correlations between predictors is important. It runs power analyses given a data generative model and an inference model. It can set up a data generative model that preserves dependence structures among variables given existing data (continuous, binary, or ordinal) or high-level descriptions of the associations. Users can generate power curves to assess the trade-offs between sample size, effect size, and power of a design. This paper presents tutorials and examples focusing on applications for environmental mixture studies when predictors tend to be moderately to highly correlated. It easily interfaces with several existing and newly developed analysis strategies for assessing associations between exposures and health outcomes. However, the package is sufficiently general to facilitate power simulations in a wide variety of settings.

翻译：估计抽样规模和统计力量是良好研究设计的一个基本部分。这个R套件使用户能够在考虑预测者之间相互关系的重要环境中,根据蒙特卡洛模拟进行电力分析。它根据数据基因模型和推理模型进行电力分析。它可以建立一个数据基因模型,根据现有数据(连续数据、二进制数据或交点数据)或协会的高级描述,在变量之间保持依赖性结构。用户可以产生动力曲线,评估抽样规模、影响大小和设计能力之间的权衡。本文介绍了在预测者往往中度和高度关联时,侧重于环境混合物研究应用的教程和实例。它很容易与若干现有和新开发的分析战略接口,用以评估接触和健康结果之间的关联。然而,该套件十分笼统,足以便利在各种环境中进行权力模拟。