A Python Code for Famispacing Estimation

Published: 16 July 2024| Version 2 | DOI: 10.17632/c3cfw72d4n.2
Kabir Bindawa Abdullahi, Yushau Sani El-Sunais, Hamza Yusuf, Musa Sani Kaware, Mohammed Suleiman, Murtala Bindawa Isah, Sama’ila Sama’ila Yar’adua, Sulaiman Sani Kankara, Abubakar Bello


Famispacing estimation, or family spacing estimation, is a quantitative method that estimates an individual's conformity or disconformity to an accepted minimum expectation (spacing interval scheme) within a population. It quantifies the similarity or dissimilarity between the observed pattern of biological children's ages and the expected pattern. Within the framework of Kabirian-based optinalysis, famispacing is conceptualized as the isoreflective pairing between the observed and expected patterns of biological children's ages. The methodological processes in famispacing comprise two distinct phases: 1) Preprocessing phase: This involves applying preprocessing operations and transformations, such as parameter distillation, theoretical ordering, and shift transformations with absolutely no data centering. It also encompasses tasks like conceptual age pattern generation and optimizations within the established optinalytic construction. These optimizations include selecting an efficient pairing style, central normalization, and establishing an isoreflective pair between the two preprocessed data. The data are the observed and expected patterns of biological children's ages. 2) Optinalytic model calculation phase: This phase is focused on computing estimates (such as the Kabirian coefficient of conformity (similarity), the probability of conformity (similarity), and the disconformity (dissimilarity)) based on Kabirian-based isomorphic optinalysis models.



Federal University Dutsin-Ma, Umaru Musa Yar'Adua University


Analysis of Algorithm, Code Reuse, Adaptive Analysis