Modality Analysis via Spacing with the Dimodal Software Libraries

Abstract

Spacing, the difference between consecutive order statistics, has two features that reflect the modality of the data. Consistent, stable values occur around modes while local increases mark the transitions between them. These features not only signal multi-modality, they also locate modes and anti-modes. Dimodal is an R package for detecting and evaluating these situations. It includes parametric feature models and bootstrap tests for spacing smoothed by low-pass filtering, non-parametric runs and permutation tests for the interval spacing, and a fusion of changepoints in the raw spacing. We introduce the analysis, describe the package, its implementation and performance, and apply it to identifying Kirkwood gaps in the asteroid belt. We also present ports of the software, with DimodalCPy a command-line program written in C with a Python interface.