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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.
| Version: | 1.0.6 |
| Imports: | Rcpp (≥ 0.12.3) |
| LinkingTo: | Rcpp |
| Suggests: | knitr |
| Published: | 2020-11-10 |
| DOI: | 10.32614/CRAN.package.fastJT |
| Author: | Jiaxing Lin, Alexander Sibley, Ivo Shterev, and Kouros Owzar |
| Maintainer: | Alexander Sibley <dcibioinformatics at duke.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | fastJT citation info |
| Materials: | NEWS |
| CRAN checks: | fastJT results |
| Reference manual: | fastJT.pdf |
| Vignettes: |
fastJT |
| Package source: | fastJT_1.0.6.tar.gz |
| Windows binaries: | r-devel: fastJT_1.0.6.zip, r-release: fastJT_1.0.6.zip, r-oldrel: fastJT_1.0.6.zip |
| macOS binaries: | r-release (arm64): fastJT_1.0.6.tgz, r-oldrel (arm64): fastJT_1.0.6.tgz, r-release (x86_64): fastJT_1.0.6.tgz, r-oldrel (x86_64): fastJT_1.0.6.tgz |
| Old sources: | fastJT archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.