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 HilbertSchmidt 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.