NovoNumeric: An Accessible, High-Performance Statistical Software for Advancing Medical Research on macOS Authors Mohammed Jaseem Ibrahim K NovoNumeric Software, Thiruvananthapuram, India https://orcid.org/0009-0009-3144-1996 DOI: https://doi.org/10.64383/irjss.20250924 Keywords: Statistical Software, Data Analysis, macOS, SwiftUI, Computational Statistics, High-Performance Computing, Accelerate Framework, Data Visualization, Principal Component Analysis, Linear Regression Abstract The field of statistical analysis is increasingly reliant on software tools that are not only powerful but also accessible and intuitive. This paper introduces NovoNumeric, a novel statistical analysis application engineered specifically for the macOS platform. Developed natively in SwiftUI, NovoNumeric provides a comprehensive, end-to-end analytical workflow within a single, responsive interface. The application's architecture is centered around a stateless analysis engine that leverages Apple's high-performance Accelerate and LAPACK frameworks for its core numerical computations, ensuring both speed and accuracy. Key features include a versatile data management suite, a library of 18 parametric and non-parametric statistical tests, advanced multivariate procedures such as Principal Component Analysis (PCA) and K-Means Clustering, and a dynamic visualization engine built on the native Charts framework. Notably, the application integrates essential diagnostic procedures, including Variance Inflation Factor (VIF) for regression and post-hoc tests for ANOVA, promoting statistical best practices. By combining a robust computational backend with a user-centric design, NovoNumeric aims to bridge the gap between complex analytical power and user accessibility, providing a valuable tool for researchers, students, and data analysts in the Apple ecosystem. Author Biography Mohammed Jaseem Ibrahim K, NovoNumeric Software, Thiruvananthapuram, India Dr. Mohammed Jaseem Ibrahim K. is a medical doctor (pharmacologist) and medical researcher specializing in creating high-performance computational tools. He is the creator of NovoNumeric, a native macOS application designed to make advanced statistical analysis intuitive for researchers and students. References [1] R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/ [2] Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261-272. [3] Apple Inc. SwiftUI. Apple Developer Documentation. [Cited 2025 Jul 10]. Available from: https://developer.apple.com/xcode/swiftui/ [4] Apple Inc. Accelerate. Apple Developer Documentation. [Cited 2025 Jul 10]. Available from: https://developer.apple.com/documentation/accelerate [5] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer; 2009. [6] Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J, et al. LAPACK Users' guide. 3rd ed. Philadelphia, PA: Society for Industrial and Applied Mathematics; 1999. [7] Belsley DA, Kuh E, Welsch RE. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons; 1980. [8] Tukey JW. The problem of multiple comparisons. Princeton, NJ: Princeton University; 1953. [9] Dunn OJ. Multiple Comparisons Among Means. J Am Stat Assoc. 1961;56(293):52-64. [10] Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3/4):591-611. [11] Arthur D, Vassilvitskii S. k-means++: The advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 2007. p. 1027-1035. [12] Apple Inc. Charts. Apple Developer Documentation. [Cited 2025 Jul 10]. Available from: https://developer.apple.com/documentation/charts Downloads PDF Published 2025-10-23 How to Cite Mohammed Jaseem Ibrahim K. (2025). NovoNumeric: An Accessible, High-Performance Statistical Software for Advancing Medical Research on macOS. International Research Journal of Scientific Studies, 2(8), 1–9. https://doi.org/10.64383/irjss.20250924 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX Issue Vol. 2 No. 8 (2025): October Section Research Papers Categories Computer Science Engineering Data Science Pharmaceutical Engineering License Copyright (c) 2025 International Research Journal of Scientific Studies This work is licensed under a Creative Commons Attribution 4.0 International License. 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