R/sparseEigen-package.R

In dppalomar/sparseEigen: Computation of Sparse Eigenvectors of a Matrix

#' sparseEigen: Computation of Sparse Eigenvectors of a Matrix
#'
#'Computation of sparse eigenvectors of a matrix (aka sparse PCA)
#'with running time 2-3 orders of magnitude lower than existing methods and
#'better final performance in terms of recovery of sparsity pattern and
#'estimation of numerical values. Can handle covariance matrices as well as
#'data matrices with real or complex-valued entries. Different levels of
#'sparsity can be specified for each individual ordered eigenvector and the
#'method is robust in parameter selection. See vignette for a detailed
#'documentation and comparison, with several illustrative examples.
#'
#' @section Functions:
#' \code{\link{spEigen}}, \code{\link{spEigenCov}}
#'
#' @section Help:
#' For a quick help see the README:
#' \href{https://rawgit.com/dppalomar/sparseEigen/master/README.html}{GitHub-README} and
#' \href{https://cran.r-project.org/web/packages/sparseEigen/README.html}{CRAN-README}.
#'
#' For more details see the vignette:
#' \href{https://rawgit.com/dppalomar/sparseEigen/master/vignettes/SparseEigenvectors-vignette.html}{GitHub-html-vignette},
#' \href{https://rawgit.com/dppalomar/sparseEigen/master/vignettes/SparseEigenvectors-vignette.pdf}{GitHub-pdf-vignette}, and
#' \href{https://cran.r-project.org/web/packages/sparseEigen/vignettes/SparseEigenvectors.pdf}{CRAN-pdf-vignette}.
#'
#' @author Konstantinos Benidis and Daniel P. Palomar
#'
#' @references
#' K. Benidis, Y. Sun, P. Babu, and D. P. Palomar, "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation,"
#' \emph{IEEE Transactions on Signal Processing}, vol. 64, no. 23, pp. 6211-6226, Dec. 2016.
#'
#' @docType package
#' @name sparseEigen-package
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