Ball

Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence doi:10.1214/17-AOS1579, ball covariance doi:10.1080/01621459.2018.1543600, and ball correlation doi:10.1080/01621459.2018.1462709, 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 doi:10.18637/jss.v097.i06. 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 doi:10.1002/gepi.22423 is supported, which is particularly helpful for genome-wide association study.