Ensemble forecasts are commonly used to support decision-making and policy planning across various fields because they often offer improved accuracy and stability compared to individual models. As each model has its own unique characteristics, understanding and measuring the value of each constituent model can support the construction of effective ensembles. The R package modelimportance provides tools to quantify how each component model contributes to the accuracy of ensemble performance for both point and probabilistic forecasts. The package supports multiple ensemble methods and multiple model importance metrics. Additionally, the software offers customizable options for handling missing values. These features enable the package to serve as a versatile tool for researchers and practitioners. It helps not only in constructing an effective ensemble model across a wide range of forecasting tasks, but also in understanding the role of each model within the ensemble and gaining insights into individual models themselves. This package follows the 'hubverse' framework, which is a collection of open-source software, tools and data standards developed to promote collaborative modeling hub efforts and simplify their setup and operation. Doing so enables seamless integration and flexibility with other forecasting tools and systems, allowing many analyses to be performed on existing hubs.

翻译：集合预报广泛应用于各领域决策与政策规划，因其相较于单个模型通常能提供更优的精度与稳定性。由于每个模型具有独特特征，理解并量化各组成模型的价值有助于构建有效的集成模型。R包modelimportance提供了一系列工具，可量化每个组成模型对点预报与概率预报的集成性能精度的贡献程度。该软件包支持多种集成方法及多种模型重要性评估指标，并提供可定制的缺失值处理选项。这些特性使其成为研究人员与从业者的通用工具，不仅有助于在广泛的预报任务中构建有效的集成模型，还能帮助理解各模型在集成中的作用并洞察单个模型的特性。本软件包遵循'hubverse'框架——一套旨在促进协作式建模中枢协作、简化其设置与运行的开源软件、工具及数据标准集合。该设计确保了与其他预报工具系统的无缝集成与灵活性，支持对现有中枢开展多样化分析。