Scientific inference is often undermined by the vast but rarely explored "multiverse" of defensible modelling choices, which can generate results as variable as the phenomena under study. We introduce RobustiPy, an open-source Python library that systematizes multiverse analysis and model-uncertainty quantification at scale. RobustiPy unifies bootstrap-based inference, combinatorial specification search, model selection and averaging, joint-inference routines, and explainable AI methods within a modular, reproducible framework. Beyond exhaustive specification curves, it supports rigorous out-of-sample validation and quantifies the marginal contribution of each covariate. We demonstrate its utility across five simulation designs and ten empirical case studies spanning economics, sociology, psychology, and medicine, including a re-analysis of widely cited findings with documented discrepancies. Benchmarking on ~672 million simulated regressions shows that RobustiPy delivers state-of-the-art computational efficiency while expanding transparency in empirical research. By standardizing and accelerating robustness analysis, RobustiPy transforms how researchers interrogate sensitivity across the analytical multiverse, offering a practical foundation for more reproducible and interpretable computational science.

翻译：科学推理常因海量但鲜少被探索的“多宇宙”中存在的合理建模选择而受到削弱，这些选择可能产生与研究现象一样多样的结果。我们推出RobustiPy，一个开源的Python库，它系统化大规模多宇宙分析与模型不确定性量化。RobustiPy在模块化、可复现框架内统一了基于自助法的推理、组合性规范搜索、模型选择与平均化、联合推理程序以及可解释人工智能方法。除了穷尽性规范曲线，它还支持严格的样本外验证，并量化每个协变量的边际贡献。我们通过五种模拟设计和十项涵盖经济学、社会学、心理学与医学的实证案例研究（包括对存在已记录差异的广受引用发现的重分析）展示了其实用性。对约6.72亿次模拟回归的基准测试表明，RobustiPy在扩展实证研究透明度的同时，提供了最先进的计算效率。通过标准化并加速稳健性分析，RobustiPy革新了研究者审视分析多宇宙中敏感性的方式，为更可复现、可解释的计算科学提供了实践基础。