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.


News

fastJT v1.0.4 (Release data: 2017-05-10)

  • Fix bugs for compile vignettes files using texlive 2017.

fastJT v1.0.3 (Release data: 2017-05-10)

  • Fix bugs for extreme small p-values for "nan" case.

fastJT v1.0.2 (Release data: 2017-02-20)

  • Fix bugs for cause NOTE in the --as-cran check for now R 3.4

fastJT v1.0.1 (Release date: 2017-02-06)

  • Fix bugs when row names of the data is null

Reference manual

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install.packages("fastJT")

1.0.4 by Jiaxing Lin, a year ago


Browse source code at https://github.com/cran/fastJT


Authors: Jiaxing Lin , Alexander Sibley , Ivo Shterev , and Kouros Owzar


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp

Suggests knitr

Linking to Rcpp


See at CRAN