fastfrechet:用于快速实现具有分布式响应的Fréchet回归的R包
Alexander Coulter1, Rebecca Lee1, Irina Gaynanova2
1Department of Statistics, Texas A&M University, United States.
Journal of open source software
|October 22, 2025
概括
本研究介绍了fastfrechet,这是用于分布对应回归的R包. 它在2-瓦瑟斯坦空间中提供快速的Fréchet回归和变量选择,增强生物医学数据分析.
科学领域:
- 统计建模 统计建模
- 计算统计的计算统计.
- 生物医学数据分析
背景情况:
- 分布作为反应回归在生物医学领域越来越重要,因为患者数据复杂.
- 弗雷切回归处理非欧几里德和分布式反应,扩展传统的回归.
- 在这个领域,现有的变量选择方法正在得到改进.
研究的目的:
- 引入Fastfrechet,一个R包,用于高效的Fréchet回归和变量选择.
- 为分析大量分布值生物医学数据集提供可扩展的实现.
- 为了使研究人员能够轻松使用先进的统计方法.
主要方法:
- 在R.中实施Fréchet回归和变量选择算法.
- 用2 - 瓦瑟斯坦度量来计算分布式反应.
- 包含用于自动变量选择的重新抽样工具.
主要成果:
- 快速frechet包为基于分布的回归提供了一个快速和可扩展的解决方案.
- 通过重新采样技术实现高效的变量选择.
- 方便分析像英国生物银行这样的大规模生物医学数据集.
结论:
- fastfrechet使先进的Fréchet回归和变量选择方法的使用变得民主化.
- 该包具有高度可扩展性,适用于大型生物医学数据.
- 它支持对复杂的分布值数据进行强有力的分析.
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