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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.
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