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dcorBSS: Distance-Correlation Based Methods for Blind Source Separation and Dependence Analysis
Independent component analysis based on distance correlation, including a robust variant using the bowl transformation. The package provides user-facing implementations of distance covariance and distance correlation, including memory-efficient blockwise computations for large data sets. It includes a sequential ICA estimator based on minimizing distance correlation, as well as tools for analyzing serial dependence via distance autocorrelation, dependograms, and permutation-based tests. In addition, it provides functions for testing serial dependence based on distance correlation and the Hilbert–Schmidt independence criterion. The methodology is related to Matteson and Tsay (2017) <doi:10.1080/01621459.2016.1150851> and to the robust framework of Leyder et al. (2026) <doi:10.1007/s11634-026-00674-9>.
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Package details |
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| Author | Sarah Leyder [aut] (ORCID: <https://orcid.org/0000-0002-0828-0399>), Klaus Nordhausen [aut, cre] (ORCID: <https://orcid.org/0000-0002-3758-8501>) |
| Maintainer | Klaus Nordhausen <klausnordhausenR@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.0-1 |
| Package repository | View on CRAN |
| Installation |
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