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fastJT  

Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies
View on CRAN: Click here


Download and install fastJT package within the R console
Install from CRAN:
install.packages("fastJT")

Install from Github:
library("remotes")
install_github("cran/fastJT")

Install by package version:
library("remotes")
install_version("fastJT", "1.0.8")



Attach the package and use:
library("fastJT")
Maintained by
Alexander Sibley
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-27
Latest Update: 2025-05-01
Description:
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.
How to cite:
Alexander Sibley (2017). fastJT: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies. R package version 1.0.8, https://cran.r-project.org/web/packages/fastJT. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0.1 (2017-02-06 21:27), 1.0.2 (2017-02-21 00:03), 1.0.3 (2017-05-11 07:59), 1.0.4 (2017-08-14 20:40), 1.0.5 (2019-07-18 18:40), 1.0.6 (2020-11-10 16:10), 1.0 (2017-01-27 10:18)
Other packages that cited fastJT R package
View fastJT citation profile
Other R packages that fastJT depends, imports, suggests or enhances
Complete documentation for fastJT
Functions, R codes and Examples using the fastJT R package
Some associated functions: fastJT-package . fastJT . fastJT.select . pvalues . summary.fastJT . 
Some associated R codes: fastJT.R . fastJT.select.R . pvalues.R . summary.fastJT.R .  Full fastJT package functions and examples
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