Package: Ball 1.3.13

Ball: Statistical Inference and Sure Independence Screening via Ball Statistics

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.

Authors:Jin Zhu [aut, cre], Wenliang Pan [aut], Yuan Tian [aut], Weinan Xiao [aut], Chengfeng Liu [aut], Ruihuang Liu [aut], Yue Hu [aut], Hongtu Zhu [aut], Heping Zhang [aut], Xueqin Wang [aut]