This research team focuses on the development of data-driven methods for prediction, such as regression, classification, and clustering. These methods employ resampling techniques, including cross-validation, bootstrapping, and permutation, to assess the quality of solutions and conduct statistical inference. They facilitate hypothesis generation and prediction in applied behavioral science.