Statistical models are at the heart of any empirical study for hypothesis testing. We present a new cross-platform Python-based package which employs different likelihood prescriptions through a 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, all in one place. Within this package, we propose a new simplified likelihood prescription that surpasses its predecessors' approximation accuracy by incorporating asymmetric uncertainties. Furthermore, our package facilitates the inclusion 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：统计模型是任何假设检验实证研究的核心。我们提出了一个基于Python的新型跨平台软件包，该软件包通过插件系统采用不同的似然函数规范。该框架使用户能够在不开发软件基础设施的情况下提出、检验和发布新的似然函数规范，最终在统一框架内兼容并概括不同似然函数构建方式及其用于假设检验的方法。在该软件包中，我们提出了一种新的简化似然函数规范，通过引入非对称不确定性超越了先前方法的近似精度。此外，我们的软件包支持集成多种似然函数组合程序，从而通过元分析扩展独立研究的范围。通过保持对似然函数规范和信号假设生成器来源的无关性，我们的平台能够无缝实现具有不同似然函数规范的软件包，促进兼容性和互操作性。