  The original Finite Selection Model (FSM) was developed in the 1970s to
enhance the design of the RAND Health Insurance Experiment (HIE; Newhouse et
al. 1993). At the time of its development by Carl Morris (Morris 1979), there
were fundamental computational limitations to make the method widely available
for practitioners. Today, as randomized experiments increasingly become more
common, there is a need for implementing experimental designs that are
randomized, balanced, robust, and easily applicable to several treatment
groups. To help address this problem, we revisit the original FSM under the
potential outcome framework for causal inference and provide its first readily
available software implementation. In this paper, we provide an introduction to
the FSM and a step-by-step guide for its use in R.
