Calibrated probabilistic models ensure that predictions are consistent with empirically observed outcomes, and hence such models provide reliable uncertainty estimates for decision-making. This is particularly important in safety-critical applications. We present Julia packages for analyzing calibration of general probabilistic predictive models, beyond commonly studied classification models. Additionally, our framework allows to perform statistical hypothesis testing of calibration.