The paper addresses the gap between methodologists and end-users in the field of statistical analysis, particularly in meta-analysis. Methodologists often develop advanced statistical methods in R, a powerful statistical programming language, but these methods are not easily accessible to non-technical users who prefer graphical user interfaces (GUIs). The authors propose a strategy to bridge this gap by using R as the computational back-end and Python for the GUI. They present OpenMeta-Analyst, an open-source meta-analysis software that leverages R's statistical capabilities and Python's GUI strengths. The framework allows methodologists to implement new methods in R, which are then automatically integrated into the Python GUI, making advanced statistical techniques accessible to non-technical users. The paper discusses the benefits of this approach, including ease of use and the ability to incorporate the latest statistical methods. The authors also provide a detailed example of how a new meta-analysis method can be implemented in R and integrated into the GUI without requiring GUI-specific coding. The paper concludes by highlighting the potential of this framework in other mathematically driven application areas and suggests future work on creating a general-purpose Python library for generating UIs for R packages.The paper addresses the gap between methodologists and end-users in the field of statistical analysis, particularly in meta-analysis. Methodologists often develop advanced statistical methods in R, a powerful statistical programming language, but these methods are not easily accessible to non-technical users who prefer graphical user interfaces (GUIs). The authors propose a strategy to bridge this gap by using R as the computational back-end and Python for the GUI. They present OpenMeta-Analyst, an open-source meta-analysis software that leverages R's statistical capabilities and Python's GUI strengths. The framework allows methodologists to implement new methods in R, which are then automatically integrated into the Python GUI, making advanced statistical techniques accessible to non-technical users. The paper discusses the benefits of this approach, including ease of use and the ability to incorporate the latest statistical methods. The authors also provide a detailed example of how a new meta-analysis method can be implemented in R and integrated into the GUI without requiring GUI-specific coding. The paper concludes by highlighting the potential of this framework in other mathematically driven application areas and suggests future work on creating a general-purpose Python library for generating UIs for R packages.