This software paper introduces a Julia implementation of the Calibrate, Emulate, Sample workflow for Bayesian parameter uncertainty quantification. The calibration stage selects informative model evaluations near parameter values that are consistent with observations. Statistical emulators then approximate the expensive parameter-to-observation map so that posterior sampling becomes cheaper. The package provides modular components for linking a user’s simulator, prior information, calibration tools, emulators, and sampling methods. Readers get an overview of the workflow and software interfaces needed to assess whether this approach can make uncertainty quantification feasible for their own model.