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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "bigPCAcpp" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'bigPCAcpp: Principal Component Analysis for ''bigmemory'' Matrices'
version: 0.9.1
doi: 10.32614/CRAN.package.bigPCAcpp
identifiers:
- type: doi
  value: 10.32614/CRAN.package.bigPCAcpp
abstract: High performance principal component analysis routines that operate directly
  on bigmemory::big.matrix() objects. The package avoids materialising large matrices
  in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers
  to derive scores, loadings, correlations, and contribution diagnostics, including
  utilities that stream results into 'bigmemory'-backed matrices for file-based workflows.
  Additional interfaces expose 'scalable' singular value decomposition, robust PCA,
  and robust SVD algorithms so that users can explore large matrices while tempering
  the influence of outliers. 'Scalable' principal component analysis is also implemented,
  Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) <https://doi.org/10.1145/2723372.2751520>.
authors:
- family-names: Bertrand
  given-names: Frederic
  email: frederic.bertrand@lecnam.net
preferred-citation:
  type: manual
  title: Principal Component Analysis for 'bigmemory' Matrices
  authors:
  - family-names: Bertrand
    given-names: Frederic
    email: frederic.bertrand@lecnam.net
  year: '2026'
  notes: R package version 0.9.1
  url: https://CRAN.R-project.org/package=bigPCAcpp
  doi: 10.32614/CRAN.package.bigPCAcpp
repository: https://fbertran.r-universe.dev
repository-code: https://github.com/fbertran/bigPCAcpp
commit: a8ed2d5f3ea7ada98b419e1e7ee3bc83425efb00
url: https://fbertran.github.io/bigPCAcpp/
date-released: '2026-03-25'
contact:
- family-names: Bertrand
  given-names: Frederic
  email: frederic.bertrand@lecnam.net