Conference Agenda

The vast majority of statisticians in academia and industry alike write statistical software daily. Nonetheless, software engineering principles are often neglected in biostatistics: most biostatisticians know a programming language (such as R) but lack formal training in writing reusable and reliable code.

This course aims to equip participants with the essential software engineering practices required to develop and maintain robust R packages. With the growing demand for reproducible research and the increasing complexity of statistical methods developed for multidimensional data, writing high-quality R packages has become a critical skill for statisticians to prototype, develop, and disseminate novel methods and push their adoption in practice. The course will focus on the key principles of software engineering, such as workflows, modular design, version control, testing, documentation, and quality indicators. Focussing on these aspects ensures the reliability and sustainability of R packages.

Participants will learn how to structure their R packages following best practices and making use of tools that streamline the development process. The course will also cover version control using Git, allowing participants to manage code changes effectively and collaborate with others. A significant emphasis will be placed on writing and running unit tests, ensuring that packages are error-free and behave as expected across different environments and over time.

Furthermore, the course will cover quality indicators for R packages and explore techniques for writing effective documentation, enabling users to pick, understand, and use statistical software packages effectively.

By the end of the course, participants will have a solid understanding of good software engineering principles tailored to R package development, enabling them to build packages that are not only functional but also reliable, reusable, and easy to maintain.