Package: sparseEigen
Title: Computation of Sparse Eigenvectors of a Matrix
Version: 0.1.0
Description: 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. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Authors@R: c(
  person("Konstantinos", "Benidis", role = "aut", email = "benidisk@gmail.com"),
  person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com")
  )
Maintainer: Daniel P. Palomar <daniel.p.palomar@gmail.com>
URL: https://github.com/dppalomar/sparseEigen,
        https://www.danielppalomar.com,
        https://doi.org/10.1109/TSP.2016.2605073
BugReports: https://github.com/dppalomar/sparseEigen/issues
Depends: R (>= 3.4.0)
License: GPL-3 | file LICENSE
Encoding: UTF-8
LazyData: true
Imports:
RoxygenNote: 6.0.1
Suggests: knitr, MASS, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-12-21 15:54:05 UTC; hornik
Author: Konstantinos Benidis [aut],
  Daniel P. Palomar [cre, aut]
Repository: CRAN
Date/Publication: 2017-12-21 16:54:39
Built: R 4.6.0; ; 2026-04-27 03:42:15 UTC; unix
