This includes a Jupyter Notebook running bumphunter (from R) with variations in parameters to see how sensitive the DMPs found are to different settings like cutoff percentile values, maximum cluster size, preprocessing method, and using Beta vs M-value as the methylation measure in the model.
This function performs the bumphunting approach described by Jaffe et al. International Journal of Epidemiology (2012). The main output is a table of candidate regions with permutation or bootstrap-based family-wide error rates (FWER) and p-values assigned.