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The EM algorithm is a method for finding the maximum likelihood estimate of amodel in the presence of missing data. Unfortunately, EM does not produce aparameter covariance matrix for standard errors. Supplemented EM (SEM; Meng &Rubin, 1991) is one method for obtaining the parameter covariance matrix. SEMis implemented in both open-source (e.g., Chalmers, 2012; Pritikin, Hunter, &Boker, 2015) and commercial (e.g., Cai, Thissen, & du Toit, 2011) item responsemodel estimation software. However, the original formulation of SEM did notadequately account for the limitations of IEEE 754 floating-point. Agile-SEM, anovel refinement of SEM, is proposed and compared against the originalalgorithm and a recent refinement (Tian, Cai, Thissen, & Xin, 2013) in avariety of item response model simulation studies. By controlling for thenumerical noise intensity on a per-parameter basis, Agile-SEM demonstrated thebest convergence properties, accuracy, and efficiency while, at the same time,requiring fewer tuning parameters. Complete source code is made freelyavailable. The potential generalization of Agile-SEM to other EM applicationbesides item response models is left as future work.

A Computational Note on the Application of the Supplemented EM Algorithm to Item Response Models