The Тexternal validityУ of a scientific finding is its robustness in the face of additional observations or alternative model specifications. Statistically significant findings can often be weakened or reversed if either the same analysis had been done with a different sample or if a different model specification had been applied to the same data. Bayesian statistical techniques are particularly well suited to address such issues, but their uptake has been impeded by their awkward, difficult implementation in the standard statistical programs most commonly used by researchers. К This grant provides funds for the continued development and adoption of Stan, an open source, probabilistic programming language developed by Columbia University statistician Andrew Gelman. Stan elegantly implements advanced Bayesian methods for analyzing external validity and for many other issues and has gained increasing popularity in recent years. Grant funds will allow the continued growth of Stan with a specific focus on developments aimed at making the program more useful and useable by economists and other social scientists. Planned grant activities include the development of new modules specifically addressing the complex and multilevel systems that social scientists study, as well as the production of open-access tutorials, research papers, and reproducible case studies.