Maurice HT Ling^{1,2 *}

¹ HOHY PTE LTD, Singapore
² Newcastle Australia Institute of Higher Education, University of Newcastle, Australia

*Corresponding Author: Maurice HT Ling, HOHY PTE LTD, Singapore and Newcastle Australia Institute of Higher Education, University of Newcastle, Australia.

Received: January 16, 2026; Published: February 13, 2026

Abstract

Python and R dominate contemporary scientific data analysis due to their mature ecosystems and extensive methodological support, yet performance limitations often arise in computation-intensive scenarios, leading to fragmented multi-language workflows. SiPy is a lightweight statistical interface written in Python that addresses this challenge by explicitly coordinating multiple languages while preserving clear execution boundaries. Building on earlier versions that integrated R as a statistical backend, this article reports SiPy 0.8.0 (released on 09 January 2026), which extends the framework to incorporate Julia as a high-performance computational engine and formalises script-level execution across Python, R, and Julia using a uniform subprocess-based model. In this three- legged architecture, Python functions both as the primary orchestration layer and as a scriptable computational backend, R provides rigorously validated statistical methods, and Julia supports performance-critical numerical computation and simulation. The system architecture underlying this design is described, including environment isolation, script-based execution, and conservative data exchange strategies that prioritise reproducibility and portability.

Keywords: Scientific Computing; Statistical Analysis; Python; R; Julia; Multi-Language Workflows; Reproducible Research; High- Performance Computing; Data Analysis Frameworks; Interoperability; External Script Execution

References

Citation

Citation: Maurice HT Ling. “SiPy 0.8.0 on the Three Legs of Python, R, and Julia". Acta Scientific Computer Sciences 7.8 (2025): 26-34.

Copyright

Copyright: © 2025 Maurice HT Ling. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.