  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.
