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Statistics \u003cimg src='https://raw.githubusercontent.com/Mamba413/git_picture/master/ball_logo.png' align=\"right\" height=\"120\" /\u003e\n===========\n\n\u003c!-- [![Travis Build Status](https://travis-ci.org/Mamba413/Ball.svg?branch=master)](https://travis-ci.org/Mamba413/Ball) --\u003e\n[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/Mamba413/Ball?branch=master\u0026svg=true)](https://ci.appveyor.com/project/Mamba413/Ball)\n[![CRAN Status Badge](http://www.r-pkg.org/badges/version/Ball)](https://CRAN.R-project.org/package=Ball)\n[![PyPI version](https://badge.fury.io/py/Ball.svg)](https://pypi.python.org/pypi/Ball/)\n\nIntrodution\n----------\nThe fundamental problems for data mining, statistical analysis, and machine learning are:\n- whether several distributions are different?\n- whether random variables are dependent?\n- how to pick out useful variables/features from a high-dimensional data?\n\nThese issues can be tackled by Ball statistics, which enjoy following admirable advantages:\n- available for most of datasets (e.g., traditional tabular data, brain shape, functional connectome, wind direction and so on)\n- insensitive to outliers, distribution-free and model-free;\n- theoretically guaranteed and computationally efficient.\n\nSoftwares\n----------\n### R package\nInstall the **Ball** package from CRAN:        \n```R\ninstall.packages(\"Ball\")\n```\nCompared with selective R packages available for datasets in metric spaces:\n\n|                                   | [fastmit](https://cran.r-project.org/web/packages/fastmit) | [energy](https://cran.r-project.org/web/packages/energy) | [HHG](https://cran.r-project.org/web/packages/HHG) | [Ball](https://cran.r-project.org/web/packages/Ball) |\n| :-------------------------------- | :----------------------------------------------------------: | :--------------------------------------------------------: | :--------------------------------------------------: | :----------------------------------------------------: |\n| Test of equal distributions       | :x:                                                        | :heavy_check_mark:                                       | :heavy_check_mark:                                 | :heavy_check_mark:                                   |\n| Test of independence              | :heavy_check_mark:                                         | :heavy_check_mark:                                       | :heavy_check_mark:                                 | :heavy_check_mark:                                   |\n| Test of joint independence        | :x:                                                        | :x:                                                      | :x:                                                | :heavy_check_mark:                                   |\n| Feature screening / Sure Independence Screening (SIS) | :x:                                                        | :x:                                                      | :x:                                                | :heavy_check_mark:                                   |\n| Iterative Feature screening / Iterative SIS                     | :x:                                                        | :x:                                                      | :x:                                                | :heavy_check_mark:                                   |\n| Datasets in metric spaces         | :heavy_check_mark:                                         | SNT                                   | :heavy_check_mark:                                 | :heavy_check_mark:                                   |\n| Robustness                        | :heavy_check_mark:                                         | :x:                                                      | :heavy_check_mark:                                 | :heavy_check_mark:                                   |\n| Parallel programming              | :x:                                                        | :x:                                                      | :heavy_check_mark:                                 | :heavy_check_mark:                                   |\n| Computational efficiency          | :running::running::running:                                | :running::running::running:                              | :running::running:                                 | :running::running::walking:                          |\n\n*SNT is the abbreviation of strong negative type.*\n\nSee the following documents for more details about the **[Ball](https://cran.r-project.org/web/packages/Ball)** package:\n- [github page](https://github.com/Mamba413/Ball/tree/master/R-package) (short)\n- [vignette](https://cran.r-project.org/web/packages/Ball/vignettes/Ball.html) (moderate)\n- [JSS paper](https://arxiv.org/abs/1811.03750) (detailed)\n\n### Python package\nInstall the **Ball** package from PyPI:        \n```shell\npip install Ball\n```\n\nCitation\n----------\nIf you use `Ball` or reference our vignettes in a presentation or publication, we would appreciate citations of our package.\n\u003e Zhu J, Pan W, Zheng W, Wang X (2021). “Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces.” Journal of Statistical Software, 97(6), 1–31. doi: 10.18637/jss.v097.i06.\n\nHere is the corresponding Bibtex entry\n```\n@Article{ball2021zhu,\n  title = {{Ball}: An {R} Package for Detecting Distribution Difference and Association in Metric Spaces},\n  author = {Jin Zhu and Wenliang Pan and Wei Zheng and Xueqin Wang},\n  journal = {Journal of Statistical Software},\n  year = {2021},\n  volume = {97},\n  number = {6},\n  pages = {1--31},\n  doi = {10.18637/jss.v097.i06},\n}\n```\n\n\nReferences\n----------\n- Pan, Wenliang; Tian, Yuan; Wang, Xueqin; Zhang, Heping. [Ball Divergence: Nonparametric two sample test](https://projecteuclid.org/euclid.aos/1525313077). Ann. Statist. 46 (2018), no. 3, 1109--1137. doi:10.1214/17-AOS1579. [https://projecteuclid.org/euclid.aos/1525313077](https://projecteuclid.org/euclid.aos/1525313077)\n- Wenliang Pan, Xueqin Wang, Weinan Xiao \u0026 Hongtu Zhu (2018) [A Generic Sure Independence Screening Procedure](https://amstat.tandfonline.com/doi/full/10.1080/01621459.2018.1462709#.WupWaoiFM2x), Journal of the American Statistical Association, DOI: 10.1080/01621459.2018.1462709\n- Wenliang Pan, Xueqin Wang, Heping Zhang, Hongtu Zhu \u0026 Jin Zhu (2019) [Ball Covariance: A Generic Measure of Dependence in Banach Space](https://doi.org/10.1080/01621459.2018.1543600), Journal of the American Statistical Association, DOI: 10.1080/01621459.2018.1543600\n- Zhu, Jin, Wenliang Pan, Wei Zheng, and Xueqin Wang. 2021. “[Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces](https://www.jstatsoft.org/article/view/v097i06)”. Journal of Statistical Software 97 (6):1-31. https://doi.org/10.18637/jss.v097.i06.\n- Wang, Xueqin, Jin Zhu, Wenliang Pan, Junhao Zhu, and Heping Zhang. 2023. “[Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces](https://www.tandfonline.com/doi/full/10.1080/01621459.2023.2277417).” Journal of the American Statistical Association 119 (548): 2772–84. doi:10.1080/01621459.2023.2277417.\n\nBug report\n----------\nOpen an [issue](https://github.com/Mamba413/Ball/issues) or send email to Jin Zhu at zhuj37@mail2.sysu.edu.cn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fball","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmamba413%2Fball","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmamba413%2Fball/lists"}