Title | fastJT: Efficient Jonckheere-Terpstra Test Statistics for Robust Machine Learning and Genome-Wide Association Studies (R) |
Publication Type | Software |
Year of Publication | 2017 |
Authors | Lin, Jiaxing, Alexander Sibley, Ivo D. Shterev, and Kouros Owzar |
Version | 1.0.4 |
Abstract | 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. |
Software Weblinks: