Statistical Inference and Sure Independence Screening via Ball Statistics
Overview
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.
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Installing Ball adds 4 packages (2 with compiled code, 1.6 MB of source tarballs). Heaviest: mvtnorm 1.0 MB, iterators 293 KB, gam 198 KB.
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Package metadata
- First published
- 2018-01-03
- Total releases
- 13 / 8 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 1.8 MB / 5 files
- Download size
- 2.3 MB
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It points to 10.18637/jss.v097.i06.
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