dcorBSS-package: dcorBSS: Distance-Correlation Based Methods for Blind Source...

In dcorBSS: Distance-Correlation Based Methods for Blind Source Separation and Dependence Analysis

dcorBSS: Distance-Correlation Based Methods for Blind Source Separation and Dependence Analysis

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2016.1150851")} and to the robust framework of Leyder et al. (2026) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11634-026-00674-9")}.

Details

Package dcorBSS implements a sequential ICA estimator based on minimizing distance correlation. Two main variants are supported:

• Classical whitening using mean and covariance together with ordinary distance correlation.

• Robust whitening combined with the bowl-transformed distance correlation criterion.

The package provides user-facing implementations of distance covariance and distance correlation, with an optional blockwise computation that avoids constructing full pairwise distance matrices and is therefore suitable for larger data sets.

For time series analysis, the package includes tools for measuring and testing serial dependence using distance autocorrelation. Lagwise dependence can be visualized via dependograms, and formal inference is provided through permutation-based tests.

Main functions

• dcorICA() – distance-correlation ICA estimator

• dcov_large(), dcov2_large(), dcor_large() – distance covariance and correlation

• dacf_curve() – distance-autocorrelation dependogram

• dcor_serial_test() – serial dependence test based on distance correlation

• hsic_serial_test() – serial dependence test based on HSIC

• bowl_transform() – nonlinear transformation

• biloop_transform() – nonlinear transformation

Author(s)

Maintainer: Klaus Nordhausen [e-mail] (ORCID)

Authors:

• Klaus Nordhausen [e-mail] (ORCID)

• Sarah Leyder (ORCID)

References

Székely, G. J., Rizzo, M. L., and Bakirov, N. K. (2007). Measuring and testing dependence by correlation of distances. Annals of Statistics, 35(6), 2769–2794. doi:10.1214/009053607000000505

Székely, G. J. and Rizzo, M. L. (2009). Brownian distance covariance. Annals of Applied Statistics, 3(4), 1236–1265. doi:10.1214/09-AOAS312.

Matteson, D. S. and Tsay, R. S. (2017). Independent component analysis via distance covariance. Journal of the American Statistical Association, 112(518), 623–637. doi:10.1080/01621459.2016.1150851

Leyder, S., Raymaekers, J., and Rousseeuw, P. J. (2026). Robust Distance Covariance. International Statistical Review, 94, 1–25. doi:10.1111/insr.70005

Leyder, S., Raymaekers, J., Rousseeuw, P. J., Van Deuren, T., and Verdonck, T. (2026). Independent component analysis by robust distance correlation. Advances in Data Analysis and Classification, 20, 727–765. doi:10.1007/s11634-026-00674-9

Fokianos, K. and Pitsillou, M. (2017). Consistent testing for pairwise dependence in time series. Technometrics, 59(2), 262–270. doi:10.1080/00401706.2016.1156024

Miettinen, J., Nordhausen, K., and Taskinen, S. (2017). Blind source separation based on joint diagonalization in R: The packages JADE and BSSasymp. Journal of Statistical Software, 76(2), 1–31. doi:10.18637/jss.v076.i02

dcorBSS documentation built on Nov. 10, 2026, 5:07 p.m.