ROA (Running Optimal Average) describes time series data. This model uses a Gaussian window function that moves through the data giving stronger weights to points close to the center of the Gaussian. Therefore, the width of the window function, delta, controls the flexibility of the model, with a small delta providing a very flexible model. The function also calculates the effective number of parameters, as a very flexible model will correspond to large number of parameters while a rigid model (low delta) has a low effective number of parameters. Knowing the effective number of parameters can be used to optimize the window width, e.g., using the Bayesian information criterion (BIC). An error envelope, which expands appropriately where there are gaps in the data, is also calculated for the model.