Ball: An R package for detecting distribution difference and association in metric spaces

TL;DR

The paper introduces the Ball R package, enabling distribution and independence tests in complex metric spaces, extending traditional methods beyond Euclidean settings with efficient algorithms and real data validation.

Contribution

It provides a new R package that implements generalized statistical tests for complex metric space data, with optimized algorithms for faster computation.

Findings

Ball package effectively analyzes spherical and matrix data.

Numerical studies demonstrate high power of the tests.

Real data analyses confirm practical utility.

Abstract

The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces. To properly analyze the data of more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces. In this paper, a publicly available R package Ball is provided to implement Ball statistical test procedures for K-sample distribution comparison and test of mutual independence in metric spaces, which extend the test procedures for two sample distribution comparison and test of independence. The tailormade algorithms as well as engineering techniques are employed on the Ball package to speed up computation to the best of our ability. Two real data analyses and several numerical studies have been performed and the results certify the powerfulness of Ball package in analyzing complex data, e.g., spherical data and symmetric positive matrix data.