We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hypothesized network structure or desired characteristics of that structure, (2) an estimation performance measure and its corresponding target value (e.g., a sensitivity of 0.6), and (3) a statistic and its corresponding target value that determines how the target value for the performance measure be reached (e.g., reaching a sensitivity of 0.6 with a probability of 0.8). The method consists of a Monte Carlo simulation step for computing the performance measure and the statistic for several sample sizes selected from an initial candidate sample size range, a curve-fitting step for interpolating the statistic across the entire candidate range, and a stratified bootstrapping step to quantify the uncertainty around the recommendation provided. We evaluated the performance of the method for the Gaussian Graphical Model, but it can easily extend to other models. The method displayed good performance, providing sample size recommendations that were, on average, within three observations of a benchmark sample size, with the highest standard deviation of 25.87 observations. The method discussed is implemented in the form of an R package called powerly, available on GitHub and CRAN. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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网络模型背景下样本量分析的通用蒙特卡罗方法。

我们介绍了在横截面网络模型的背景下计算样本量的通用方法。该方法采用自动蒙特卡罗算法的形式，旨在找到最佳样本大小，同时迭代地将计算集中在看起来最相关的样本大小上。该方法需要三个输入：（1）假设的网络结构或该结构的期望特性，（2）估计性能测量及其相应的目标值（例如，0.6的灵敏度），以及（3）统计量及其相应的目标值，确定如何达到绩效衡量的目标值（例如，以 0.8 的概率达到 0.6 的灵敏度）。该方法包括：蒙特卡罗模拟步骤，用于计算从初始候选样本大小范围中选择的多个样本大小的性能度量和统计量；曲线拟合步骤，用于在整个候选范围内插值统计量；以及分层引导步骤量化所提供建议的不确定性。我们评估了该方法针对高斯图模型的性能，但它可以轻松扩展到其他模型。该方法表现出良好的性能，提供的样本量建议平均在基准样本量的三个观测值之内，最高标准偏差为 25.87 个观测值。所讨论的方法以名为 powerly 的 R 包的形式实现，可在 GitHub 和 CRAN 上获取。（PsycInfo 数据库记录 (c) 2023 APA，