R/dcorBSS-package.R

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
#'
#' @section Main functions:
#' \itemize{
#'   \item [dcorICA()] -- distance-correlation ICA estimator
#'   \item [dcov_large()], [dcov2_large()], [dcor_large()] -- distance covariance and correlation
#'   \item [dacf_curve()] -- distance-autocorrelation dependogram
#'   \item [dcor_serial_test()] -- serial dependence test based on distance correlation
#'   \item [hsic_serial_test()] -- serial dependence test based on HSIC
#'   \item [bowl_transform()] -- nonlinear transformation
#'   \item [biloop_transform()] -- nonlinear transformation
#' }
#'
#' @details
#' Package \pkg{dcorBSS} implements a sequential ICA estimator based on
#' minimizing distance correlation. Two main variants are supported:
#' \itemize{
#'   \item Classical whitening using mean and covariance together with
#'   ordinary distance correlation.
#'   \item 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.
#'
#' @references
#' Székely, G. J., Rizzo, M. L., and Bakirov, N. K. (2007).
#' Measuring and testing dependence by correlation of distances.
#' \emph{Annals of Statistics}, \strong{35}(6), 2769--2794.
#' <doi:10.1214/009053607000000505>
#'
#' Székely, G. J. and Rizzo, M. L. (2009).
#' Brownian distance covariance.
#' \emph{Annals of Applied Statistics}, \strong{3}(4), 1236--1265.
#' <doi:10.1214/09-AOAS312>.
#'
#' Matteson, D. S. and Tsay, R. S. (2017).
#' Independent component analysis via distance covariance.
#' \emph{Journal of the American Statistical Association},
#' \strong{112}(518), 623--637.
#' <doi:10.1080/01621459.2016.1150851>
#'
#' Leyder, S., Raymaekers, J., and Rousseeuw, P. J. (2026).
#' Robust Distance Covariance.
#' \emph{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.
#' \emph{Advances in Data Analysis and Classification}, \strong{20}, 727--765.
#' <doi:10.1007/s11634-026-00674-9>
#'
#' Fokianos, K. and Pitsillou, M. (2017).
#' Consistent testing for pairwise dependence in time series.
#' \emph{Technometrics}, \strong{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.
#' \emph{Journal of Statistical Software}, \strong{76}(2), 1--31.
#' <doi:10.18637/jss.v076.i02>
#'
#' @name dcorBSS-package
#' @keywords internal
"_PACKAGE"