This manual describes an implementation of optimal Bayesian experimental design methods. These methods address routine measurements where data are fit to experimental models in order to obtain model parameters. The twin benefits of these methods are reduced uncertainty with fewer required measurements. These methods are therefore most beneficial in measurements where measurements are expensive in terms of money, time, risk, labor and/or discomfort. The price for these benefits lies in the complexity of automating such measurements and in the computational load required. It is the goal of this package to assist potential users in overcoming at least the programming hurdles.