In this course, faculty will cover the steps and decisions involved in calibrating a mathematical model in R. Faculty will begin the course with an overview of when model calibration is necessary and will introduce a general model calibration framework. They will then engage students in an extensive hands-on exercise where they will implement the calibration of a simple mathematical model in R using a simple random search. Faculty will then introduce more advanced calibration approaches, including Latin hypercube sampling, directed search algorithms (e.g., Nelder-Mead), Bayesian calibration, and other iterative calibration approaches (e.g., genetic algorithms). Faculty will discuss the tradeoffs of different calibration approaches and will identify scenarios when one approach may be more appropriate than others. This course is intended for individuals familiar with mathematical models (e.g., Markov models, infectious disease models, and/or microsimulation) and their application. Participants are assumed to be proficient in R and required to bring their personal laptops with the latest versions of R and RStudio installed.