To cite 'TRexSelector' in publications, please use the package entry and the paper of the method you used:
TRexSelector Team (2026). TRexSelector: T-Rex Selector: High-Dimensional Variable Selection & FDR Control. R package version 2.0.0, https://github.com/trexselector/TRexSelector.
Machkour J, Muma M, Palomar DP (2025). “The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control.” Signal Process., 231, 109894. doi:10.1016/j.sigpro.2025.109894.
Machkour J, Muma M, Palomar DP (2025). “High-Dimensional False Discovery Rate Control for Dependent Variables.” Signal Process., 234, 109990. doi:10.1016/j.sigpro.2025.109990.
Machkour J, Muma M, Palomar DP (2022). “False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter.” In Proc. 30th Eur. Signal Process. Conf. (EUSIPCO), 892-896.
Machkour J, Muma M, Palomar DP (2023). “The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research.” In Proc. IEEE 9th Int. Workshop Comput. Adv. Multi-Sensor Adapt. Process. (CAMSAP), 466-470.
Machkour J, Muma M, Palomar DP (2023). “False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks.” In Proc. 22nd IEEE Statist. Signal Process. Workshop (SSP), 666-670.
Machkour J, Breloy A, Muma M, Palomar DP, Pascal F (2024). “Sparse PCA with False Discovery Rate Controlled Variable Selection.” In Proc. IEEE 49th Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 9716-9720.
Scheidt F, Machkour J, Muma M (2023). “Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop.” In Proc. IEEE 9th Int. Workshop Comput. Adv. Multi-Sensor Adapt. Process. (CAMSAP), 116-120.
Scheidt F, Muma M (2026). “Terminating Greedy Pursuits for High-Dimensional False Discovery Rate Control.” In Proc. Eur. Signal Process. Conf. (EUSIPCO), 2511-2515.
Koka T, Machkour J, Palomar DP, Muma M (2026). “Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features.” arXiv:2604.07464.
Machkour J, Palomar DP, Muma M (2025). “FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking.” In Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP). doi:10.1109/ICASSP49660.2025.10890629.
Machkour J, Muma M, Palomar DP (2024). “FDR-Controlled Sparse Index Tracking with Autoregressive Stock Dependency Models.” In Proc. Eur. Signal Process. Conf. (EUSIPCO), 2662-2666.
The library builds on Eigen: G. Guennebaud, B. Jacob et al., Eigen v3, https://libeigen.gitlab.io/, 2010.
Corresponding BibTeX entries:
@Manual{,
title = {{TRexSelector: T-Rex Selector: High-Dimensional Variable
Selection & FDR Control}},
author = {{TRexSelector Team}},
note = {R package version 2.0.0},
year = {2026},
url = {https://github.com/trexselector/TRexSelector},
}
@Article{,
title = {The Terminating-Random Experiments Selector: Fast
High-Dimensional Variable Selection with False Discovery Rate
Control},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
journal = {Signal Process.},
volume = {231},
pages = {109894},
year = {2025},
doi = {10.1016/j.sigpro.2025.109894},
}
@Article{,
title = {High-Dimensional False Discovery Rate Control for
Dependent Variables},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
journal = {Signal Process.},
volume = {234},
pages = {109990},
year = {2025},
doi = {10.1016/j.sigpro.2025.109990},
}
@InProceedings{,
title = {False Discovery Rate Control for Grouped Variable
Selection in High-Dimensional Linear Models Using the T-Knock
Filter},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
booktitle = {Proc. 30th Eur. Signal Process. Conf. (EUSIPCO)},
pages = {892-896},
year = {2022},
}
@InProceedings{,
title = {The Informed Elastic Net for Fast Grouped Variable
Selection and FDR Control in Genomics Research},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
booktitle = {Proc. IEEE 9th Int. Workshop Comput. Adv. Multi-Sensor
Adapt. Process. (CAMSAP)},
pages = {466-470},
year = {2023},
}
@InProceedings{,
title = {False Discovery Rate Control for Fast Screening of
Large-Scale Genomics Biobanks},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
booktitle = {Proc. 22nd IEEE Statist. Signal Process. Workshop
(SSP)},
pages = {666-670},
year = {2023},
}
@InProceedings{,
title = {Sparse PCA with False Discovery Rate Controlled Variable
Selection},
author = {Jasin Machkour and Arnaud Breloy and Michael Muma and
Daniel P. Palomar and Frédéric Pascal},
booktitle = {Proc. IEEE 49th Int. Conf. Acoust., Speech, Signal
Process. (ICASSP)},
pages = {9716-9720},
year = {2024},
}
@InProceedings{,
title = {Solving FDR-Controlled Sparse Regression Problems with
Five Million Variables on a Laptop},
author = {Fabian Scheidt and Jasin Machkour and Michael Muma},
booktitle = {Proc. IEEE 9th Int. Workshop Comput. Adv. Multi-Sensor
Adapt. Process. (CAMSAP)},
pages = {116-120},
year = {2023},
}
@InProceedings{,
title = {Terminating Greedy Pursuits for High-Dimensional False
Discovery Rate Control},
author = {Fabian Scheidt and Michael Muma},
booktitle = {Proc. Eur. Signal Process. Conf. (EUSIPCO)},
pages = {2511-2515},
year = {2026},
}
@Misc{,
title = {Virtual Dummies: Enabling Scalable FDR-Controlled Variable
Selection via Sequential Sampling of Null Features},
author = {Taulant Koka and Jasin Machkour and Daniel P. Palomar and
Michael Muma},
note = {arXiv:2604.07464},
year = {2026},
}
@InProceedings{,
title = {FDR-Controlled Portfolio Optimization for Sparse Financial
Index Tracking},
author = {Jasin Machkour and Daniel P. Palomar and Michael Muma},
booktitle = {Proc. IEEE Int. Conf. Acoust., Speech, Signal Process.
(ICASSP)},
year = {2025},
doi = {10.1109/ICASSP49660.2025.10890629},
}
@InProceedings{,
title = {FDR-Controlled Sparse Index Tracking with Autoregressive
Stock Dependency Models},
author = {Jasin Machkour and Michael Muma and Daniel P. Palomar},
booktitle = {Proc. Eur. Signal Process. Conf. (EUSIPCO)},
pages = {2662-2666},
year = {2024},
}