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# CITATION.cff 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 "spca" in publications use:'
type: software
license: AGPL-3.0-only
title: 'spca: Least Squares Sparse Principal Components Analysis'
version: 1.1.1
doi: 10.1111/anzs.12128
abstract: Implements least-squares sparse principal component analysis (LS-SPCA).
  The approach follows Merola (2015) <https://doi.org/10.1111/anzs.12128> and Merola
  and Chen (2019) <https://doi.org/10.1016/j.jmva.2019.04.001>.
authors:
- family-names: Merola
  given-names: Giovanni Maria
  email: merolagio@gmail.com
preferred-citation:
  type: article
  title: 'Least Squares Sparse Principal Component Analysis: a Backward Elimination
    approach to attain large loadings'
  authors:
  - family-names: Merola
    given-names: Giovanni Maria
    email: merolagio@gmail.com
  journal: Australia & New Zealand Journal of Statistics
  year: '2015'
  volume: '57'
  doi: 10.1111/anzs.12128
  start: '391'
  end: '429'
repository: https://merolagio.r-universe.dev
repository-code: https://github.com/merolagio/spca
commit: 5f0f2aa47dcb2f4024ff41dfb74de870ebdd2ff6
url: https://github.com/merolagio/spca
date-released: '2026-06-03'
contact:
- family-names: Merola
  given-names: Giovanni Maria
  email: merolagio@gmail.com
references:
- type: article
  title: 'Projection sparse principal component analysis: An efficient least squares
    method'
  authors:
  - family-names: Merola
    given-names: Giovanni Maria
  - family-names: Chen
    given-names: Gemai
  journal: Journal of Multivariate Analysis
  year: '2019'
  volume: '173'
  doi: 10.1016/j.jmva.2019.04.001
  start: '366'
  end: '382'