Analysing statistical models is 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, enabling the statistical inference of hypotheses. This framework empowers users to propose, examine, and publish new likelihood prescriptions without the need for developing a new inference system. Within this package, we propose a new simplified likelihood prescription which surpasses the approximation accuracy of its predecessors by incorporating asymmetric uncertainties. Furthermore, 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：统计分析模型是任何实证研究中假设检验的核心。我们提出了一种全新的跨平台Python包，通过插件系统整合不同似然函数构建方案，实现了假设的统计推断。该框架使用户能够提出、检验并发布新型似然函数构建方案，而无需开发全新推断系统。在该包中，我们提出了一种通过整合非对称不确定性来超越前序方法近似精度的简化似然构建方案。此外，我们的工具包支持多种似然组合程序的集成，通过元分析扩展了独立研究的适用范围。通过保持对似然函数来源和信号假设生成器的无偏性，我们的平台能够无缝实现具有不同似然函数构建方案的软件包，从而促进兼容性与互操作性。