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>.
| Version: |
1.0-0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dccpp, dHSIC, minqa, nloptr, stats, utils, Rcpp |
| LinkingTo: |
Rcpp |
| Suggests: |
energy, JADE, robustbase, knitr, rmarkdown |
| Published: |
2026-06-10 |
| DOI: |
10.32614/CRAN.package.dcorBSS |
| Author: |
Sarah Leyder 
[aut],
Klaus Nordhausen
[aut, cre] |
| Maintainer: |
Klaus Nordhausen <klausnordhausenR at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
ChangeLog |
| CRAN checks: |
dcorBSS results |
Documentation:
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