The T-Rex is a new data science framework to provide fast and robust solutions for large-scale high-dimensional settings. T-Rex stands for terminated-random experiments. T-Rex methods can be applied to diverse areas, such as, genomics, financial engineering, or signal processing.
Our first developed T-Rex method is the T-Rex selector, which uses the T-LARS algorithm to perform fast variable selection in large-scale high-dimensional settings. The T-Rex selector provably controls the false discovery rate (FDR), i.e., the expected fraction of selected false positives among all selected variables, at the user-defined target level. In addition to controlling the FDR, it also achieves a high true positive rate (TPR), i.e., power, by maximizing the number of selected variables.
By clicking onto the titles below, you will find further information including examples and descriptions of how to install and use our developed R-packages.
For further information, please visit our Github site.