This example demonstrates how to perform a repeated-measures ANOVA with sphericity correction using a custom Python function. It shows the statistical results, including F-values, p-values, and corrected degrees of freedom, for each main effect and interaction term in a factorial within-subject design. The implementation relies on NumPy for matrix operations, SciPy for statistical distributions and detrending, and constructs effect codings through binary patterns. The code defines a `repanova` function that returns a list of named tuples containing the effect name, corrected degrees of freedom, F-value, and p-value, with optional Greenhouse-Geisser correction.