  Repeated measures analyses require proper choice of the correlation model to
ensure accurate inference and optimal efficiency. The linear exponent
autoregressive (LEAR) correlation model provides a flexible two-parameter
correlation structure that accommodates a variety of data types in which the
correlation within-sampling unit decreases exponentially in time or space. The
LEAR model subsumes three classic temporal correlation structures, namely
compound symmetry, continuous-time AR(1), and MA(1), while maintaining
parsimony and providing appealing statistical and computational properties. It
also supplies a plausible correlation structure for power analyses across many
experimental designs. However, no commonly used statistical packages provide a
straightforward way to implement the model, limiting its use to those with the
appropriate programming skills. Here we present a reparameterization of the
LEAR model that allows easily implementing it in standard software for the
special case of data with equally spaced temporal or spatial intervals.
