The identification of domain sets whose outcomes belong to predefined subsets can address fundamental risk assessment challenges in climatology and medicine. Existing approaches for inverse domain estimates require restrictive assumptions, including domain density and continuity of function near thresholds, and large-sample guarantees, which limit the applicability. Besides, the estimation and coverage depend on setting a fixed threshold level, which is difficult to determine. Recently, Ren et al. (2024) proved that confidence sets of multiple levels can be simultaneously constructed with the desired confidence non-asymptotically through inverting simultaneous confidence bands. Here, we present the SCoRES R package, which implements Ren's approach for both the estimation of the inverse region and the corresponding simultaneous outer and inner confidence regions, along with visualization tools. Besides, the package also provides functions that help construct SCBs for regression data, functional data and geographical data. To illustrate its broad applicability, we present three rigorous examples that demonstrate the SCoRES workflow.

翻译：识别其输出属于预定义子集的域集，能够解决气候学和医学中基本的风险评估挑战。现有的逆域估计方法需要严格的假设，包括域密度和函数在阈值附近的连续性，以及大样本保证，这限制了其适用性。此外，估计和覆盖依赖于设定固定的阈值水平，而该水平难以确定。最近，Ren等人（2024）证明，通过反转同步置信带，可以在非渐近情况下以所需置信度同时构建多个水平的置信集。本文介绍了SCoRES R包，该包实现了Ren的方法，用于逆区域的估计以及相应的同步外部和内部置信区域，并提供了可视化工具。此外，该包还提供了帮助为回归数据、函数数据和地理数据构建同步置信带的函数。为展示其广泛适用性，我们提供了三个严谨的示例，演示了SCoRES的工作流程。