Package: sparsepca
Type: Package
Title: Sparse Principal Component Analysis
Version: 1.0.0
Authors@R: c(
    person("N. Benjamin", "Erichson", role = c("aut", "cre"),
           email = "erichson@icsi.berkeley.edu"),
    person("Peng", "Zheng", role = "aut"),
    person("Sasha", "Aravkin", role = "aut"),
    person("Maria A.", "Sans-Fuentes", role = "ctb",
           comment = c(ORCID = "0000-0002-6402-9211",
                       "Reported the robust objective error")))
Description: Fits principal components with sparse loading vectors for
    interpretable dimension reduction. Variable projection combines an
    orthogonal reconstruction factor with elastic net or cardinality
    regularization of the loadings. Deterministic, randomized, and robust
    algorithms support exploratory analysis of high-dimensional data;
    the randomized algorithm also supports sparse input matrices through
    implicit centering and scaling. A robust formulation separates
    entrywise outliers from the low-rank reconstruction. Regularization
    paths and approximate feature-count searches reuse matrix factorizations,
    and prediction applies the fitted transformation to new observations.
    The methods are described in
    Erichson et al. (2020) <doi:10.1137/18M1211350>.
License: GPL (>= 3)
Depends: R (>= 4.4.0)
Imports: Matrix (>= 1.7-0), methods, rsvd (>= 2.0.0)
Suggests: testthat (>= 3.3.2)
Encoding: UTF-8
URL: https://github.com/erichson/spca
BugReports: https://github.com/erichson/spca/issues
NeedsCompilation: no
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.1
Packaged: 2026-11-03 22:55:30 UTC; runner
Author: N. Benjamin Erichson [aut, cre],
  Peng Zheng [aut],
  Sasha Aravkin [aut],
  Maria A. Sans-Fuentes [ctb] (ORCID:
    <https://orcid.org/0000-0002-6402-9211>, Reported the robust
    objective error)
Maintainer: N. Benjamin Erichson <erichson@icsi.berkeley.edu>
Repository: CRAN
Date/Publication: 2026-11-06 13:30:02 UTC
