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The Robust Data Science Group is committed to reproducible research. An important contribution to making scientific analyses reproducible is sharing well documented codes.
The T-Rex Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control (R)
An R-Package implementing the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. Please make reference to our work: J. Machkour, M. Muma, and D. P. Palomar, “The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control”, Preprint at arXiv:2110.06048.
The T-Lars Algorithm: A High-Dimensional Forward Variable Selection Method (R)
An R-Package implementing the Terminating-Lars algorithm. The T-Lars algorithm terminates the Lars algorithm using dummy variables. It is a building block for the T-Rex, and it allows for very fast and high-dimensional variable selection. Please make reference to our work: J. Machkour, M. Muma, and D. P. Palomar, “The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control”, Preprint at arXiv:2110.06048.
Fast and Sample Accurate R-Peak Detection for Noisy ECG Using Visibility Graphs (Python)
A Python implementation of our paper on R-peak detection method for ECG-signals that builds upon the visibility graph transformation. It allows us to assign each time point, represented by a vertex in the visibility graph of the signal, a node property that can be used to weight the sample corresponding to said vertex. From the real data experiments, we can conclude that the method in all its simplicity outperforms common detectors on a sample accurate database.
A community detection method that uses spectral partitioning based on estimating a robust and sparse graph model. Please make reference to our work: A. Tastan, M. Muma, A. M. Zoubir, “Sparsity-aware Robust Community Detection (SPARCODE)”, Signal Processing 2021.
No longer maintained: Robust Signal Processing Toolbox (Matlab, Python, R)
A toolbox for robust signal processing that can be freely used for non-commercial use only. Please make appropriate references to our book: Zoubir, A. M., Koivunen, V., Ollila, E., and Muma, M. Robust Statistics for Signal Processing Cambridge University Press, 2018.
No longer maintained: Robust Cluster Enumeration Algorithms (Matlab, Python,R)
A set of toolboxes on robust cluster analysis using Bayesian cluster enumeration criteria, heavy-tailed mixture models and M-estimation. Please make reference to our works, e.g., F. K. Teklehaymanot, M. Muma, and A. M. Zoubir, „Bayesian Cluster Enumeration Criterion for Unsupervised learning“, IEEE Trans. Signal Proc. 2018, and C. A. Schroth, and M. Muma, „Robust M-Estimation based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions“ IEEE Trans. Signal Proc. 2021
No longer maintained: ECG Motion Artifact Removal (Matlab)
An algorithm for motion artifact removal in ECG signals. Please make reference to our work: F. Strasser, M. Muma, and A. M. Zoubir, “Motion artifact removal in ECG signals using multi-resolution thresholding.” In Proc. European Signal Processing Conference (EUSIPCO) 2012.