Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilise different likelihood prescriptions via a flexible plug-in system. This framework empowers users to propose, examine, and publish new likelihood prescriptions without developing software infrastructure, ultimately unifying and generalising different ways of constructing likelihoods and employing them for hypothesis testing within a unified platform. We propose a new simplified likelihood prescription, surpassing previous approximation accuracies by incorporating asymmetric uncertainties. Moreover, our package facilitates the integration of various likelihood combination routines, thereby broadening the scope of independent studies through a meta-analysis. By remaining agnostic to the source of the likelihood prescription and the signal hypothesis generator, our platform allows for the seamless implementation of packages with different likelihood prescriptions, fostering compatibility and interoperability.

翻译：统计模型是实证研究中假设检验的基石。本文介绍了一个新的跨平台Python软件包，该软件包通过灵活的插件系统实现对不同似然形式的利用。该框架使用户能够在不开发软件基础设施的情况下提出、检验并发布新的似然形式，最终在统一平台内实现不同似然构建方式及其在假设检验中应用的统一与泛化。我们提出了一种新的简化似然形式，通过纳入非对称不确定性超越了以往的近似精度。此外，我们的软件包促进了多种似然组合例程的集成，从而通过元分析拓展了独立研究的范畴。通过保持对似然形式来源及信号假设生成器的无关性，我们的平台能够无缝实现不同似然形式的软件包集成，提升了兼容性与互操作性。