Python Code for Statistical Mirroring-based Ordinalysis

Within the framework of Kabirian-based optinalysis [1] and statistical mirroring [2], Statistical mirroring-based ordinalysis is conceptualized as the isoreflectivity (isoreflective pairing) of the composite set of ordinal assessment scores of an individual to the highest positive ordinal scale point of an established ordinal assessment scale, under customized and optimized choice of parameters.

This represents the underlying assumption of statistical mirroring-based ordinalysis. The process of Statistical mirroring-based ordinalysis comprises three distinct phases: a) Adaptive customization and optimization phase [3]: This phase represents the core of the methodology. This involves the adaptive customization and optimization of parameters to suit the requirements for statistical mirroring estimation in the given task [3]. b) Statistical mirroring computation phase [2]: This involves applying the adopted statistical mirroring type based on the phase 1 adaption. c) Optinalytic model calculation phase [1]: This phase is focused on computing estimates (such as the Kabirian coefficient of proximity, the probability of proximity, and the deviation) based on Kabirian-based isomorphic optinalysis models.