Why Risk it, When You Can {rix} it: A Tutorial for Computational Reproducibility Focused on Simulation Studies

Computational reproducibility remains limited in psychological research, despite widespreadnorms for sharing data and analysis code. These materials alone rarely suffice: re-executing ananalysis requires reconstructing the computational environment it depends on, from theprogramming language version and packages to system-level dependencies. In this article, weintroduce Nix and the {rix} R package as a practical framework for achieving computationalreproducibility in simulation-based research. We provide a step-by-step tutorial demonstratinghow {rix} can be used to define, build, and share isolated, project-specific software environmentsthat precisely capture R versions, package dependencies, system libraries, and integrateddevelopment environments. We further illustrate this workflow by reproducing a completemanuscript using Quarto and the {apaquarto} extension, showing how analyses, figures, and textcan be regenerated in a single, executable pipeline. Together, these tools lower the technicalbarrier to robust, end-to-end reproducibility and offer a scalable solution for simulation studiesand methodological research in psychology and related fields.