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：科学推理常因存在大量却鲜被系统探索的"多宇宙"（即多种可辩护的建模选择）而受到损害，这些选择可能产生与研究现象本身同样多样的结果。我们介绍了RobustiPy，一个开源的Python库，它能够大规模系统化地进行多宇宙分析与模型不确定性量化。RobustiPy在一个模块化、可复现的框架内，统一了基于自助法的推断、组合式规范搜索、模型选择与平均化、联合推断程序以及可解释人工智能方法。除了生成完备的规范曲线外，该库还支持严格的样本外验证，并能量化每个协变量的边际贡献。我们通过五个模拟实验设计和十个涵盖经济学、社会学、心理学和医学的实证案例研究（包括对存在已记录差异的广为人知发现的重新分析）展示了其实用性。在约6.72亿次模拟回归上的基准测试表明，RobustiPy在提升实证研究透明度的同时，实现了最先进的计算效率。通过标准化和加速稳健性分析，RobustiPy改变了研究人员在分析多宇宙中审视敏感性的方式，为提升计算科学的可复现性与可解释性提供了实用基础。