Ball

Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence , ball covariance , and ball correlation , are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces . Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence is supported, which is particularly helpful for genome-wide association study.

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