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fastJT: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core computing to reduce overall processing time.
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Package details |
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| Author | Jiaxing Lin, Alexander Sibley, Ivo Shterev, and Kouros Owzar |
| Maintainer | Jiaxing Lin <[email protected]> |
| License | GPL (>= 2) |
| Version | 1.0.4 |
| Package repository | View on CRAN |
| Installation |
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