In this article, we review four software packages for implementing propensity score analysis in R: "Matching, MatchIt, PSAgraphics," and "twang." After briefly discussing essential elements for propensity score analysis, we apply each package to a data set from the Early Childhood Longitudinal Study in order to estimate the average effect of elementary school special education services on math achievement in fifth grade. In the context of this real data example, we evaluate documentation and support resources, built-in quantitative and graphical diagnostic features, and methods available for estimating a causal effect. We conclude by making some recommendations aimed at helping researchers decide which package to turn to based upon their familiarity with propensity score methods, programming in R, and the type of analysis being conducted.