  The generalized persistence (GP) model, developed in the context of
estimating ``value added'' by individual teachers to their students' current
and future test scores, is one of the most flexible value-added models in the
literature. Although developed in the educational setting, the GP model can
potentially be applied to any structure where each sequential response of a
lower-level unit may be associated with a different higher-level unit, and the
effects of the higher-level units may persist over time. The flexibility of the
GP model, however, and its multiple membership random effects structure lead to
computational challenges that have limited the model's availability. We develop
an EM algorithm to compute maximum likelihood estimates efficiently for the GP
model, making use of the sparse structure of the random effects and error
covariance matrices. The algorithm is implemented in the package GPvam in R
statistical software. We give examples of the computations and illustrate the
gains in computational efficiency achieved by our estimation procedure.
