person(given = "Jin", family = "Zhu", email = "zhuj37@mail2.sysu.edu.cn", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8550-5822")),
person(given = "Wenliang", family = "Pan", role = c("aut")),
person(given = "Yuan", family = "Tian", role = c("aut")),
person(given = "Weinan", family = "Xiao", role = c("aut")),
person(given = "Chengfeng", family = "Liu", role = c("aut")),
person(given = "Ruihuang", family = "Liu", role = c("aut")),
person(given = "Yue", family = "Hu", role = c("aut")),
person(given = "Hongtu", family = "Zhu", role = c("aut")),
person(given = "Heping", family = "Zhang", role = c("aut")),
person(given = "Xueqin", family = "Wang", email = "wangxq20@ustc.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0001-5205-9950"))
Description: 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.

Description: Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces <arXiv:1811.03750>. Furthermore, a generic non-parametric SIS procedure based on ball correlation and all of its variants are implemented to tackle the challenge in the context of ultra high dimensional data.