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：集合预报因相较单个模型通常具有更高的准确性和稳定性，被广泛应用于各领域决策与政策规划。由于每个模型具有独特特征，理解并衡量各组成模型的贡献价值有助于构建高效集合。R语言包modelimportance提供了量化各组件模型对点预报与概率预报集合性能准确性的工具：该软件支持多种集合方法与多项模型重要性评估指标，并具备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，既能协助在广泛的预报任务中构建有效集合模型，又能阐明各模型在集合中的作用与内在特征。本包遵循"hubverse"框架——该框架集合了开源软件、工具与数据标准，旨在促进协作式建模枢纽建设并简化其部署运维。由此实现与其它预报工具系统的无缝集成与灵活交互，支持对现有枢纽开展多维度分析。