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.

Version: 1.0.3
Imports: Rcpp (≥ 0.12.3)
LinkingTo: Rcpp
Suggests: knitr
Published: 2017-05-11
Author: Jiaxing Lin, Alexander Sibley, Ivo Shterev, and Kouros Owzar
Maintainer: Jiaxing Lin <jiaxing.lin at duke.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: fastJT results

Downloads:

Reference manual: fastJT.pdf
Vignettes: fastJT
Package source: fastJT_1.0.3.tar.gz
Windows binaries: r-devel: fastJT_1.0.3.zip, r-release: fastJT_1.0.3.zip, r-oldrel: fastJT_1.0.3.zip
OS X El Capitan binaries: r-release: fastJT_1.0.3.tgz
OS X Mavericks binaries: r-oldrel: fastJT_1.0.3.tgz
Old sources: fastJT archive

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