Researchers at the Nuffield Department of Population Health’s Demographic Science Unit and the Leverhulme Centre for Demographic Science have developed RobustiPy, a new open-source tool, to ensure research is robust and reliable. RobustiPy was formally introduced in a paper published in Patterns today.
Scientific modelling can produce a range of possible outcomes due to the variation that exists in all environments. To see how reliable their findings are, researchers often complete robustness tests. A robust finding should deliver the same result when slight changes are introduced, such as to the method, or the adding of new data into a model.
Developed for a commonly used programming language called Python, RobustiPy represents the first in the next generation of robustness tools that enable researchers to perform rigorous tests on their findings using an unlimited number of variables and modelling choices. By combining tools that researchers would otherwise have to independently write or assemble separately, RobustiPy makes it easier to perform large-scale robustness analysis.