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.
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