True and Error Theory (TET) provides a method to separate the variability of behavior into components due to changing true policy and to random error. TET is a testable theory that can serve as a statistical model, allowing one to evaluate substantive theories as nested, special cases. TET is more accurate descriptively and has theoretical advantages over previous approaches. This paper presents a freely available computer program in R that can be used to fit and evaluate both TET and substantive theories that are special cases of it. The program performs Monte Carlo simulations to generate distributions of test statistics and bootstrapping to provide confidence intervals on parameter estimates. Use of the program is illustrated by a reanalysis of previously published data testing whether what appeared to be violations of Expected Utility (EU) theory (Allais paradoxes) by previous methods might actually be consistent with EU theory.
