Gofedf: an R package for testing goodness-of-fit tests based on the empirical distribution function

We introduce an R package for goodness-of-fit testing based on the empirical distribution function (EDF) in the presence of parameter estimation, which can be applied to any general likelihood model. The computation of the p value in goodness-of-fit tests based on EDF with parameter estimation depends on the limiting large-sample covariance function of a stochastic process. This function relies on key elements of the model, including the Fisher information matrix and the derivatives of the cumulative distribution function under the null hypothesis. Computing these elements is often not straightforward and can be computationally intensive or impractical in some cases. Instead we propose a new method to estimate the covariance function of the process directly from the sample. We provide computational tools as an R package for practical implementation.