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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 "nsprcomp" in publications use:'
type: software
title: 'nsprcomp: Non-Negative and Sparse PCA'
version: 0.5.1-2
doi: 10.1145/1390156.1390277
identifiers:
- type: doi
  value: 10.32614/CRAN.package.nsprcomp
abstract: Two methods for performing a constrained principal component analysis (PCA),
  where non-negativity and/or sparsity constraints are enforced on the principal axes
  (PAs). The function 'nsprcomp' computes one principal component (PC) after the other.
  Each PA is optimized such that the corresponding PC has maximum additional variance
  not explained by the previous components. In contrast, the function 'nscumcomp'
  jointly computes all PCs such that the cumulative variance is maximal. Both functions
  have the same interface as the 'prcomp' function from the 'stats' package (plus
  some extra parameters), and both return the result of the analysis as an object
  of class 'nsprcomp', which inherits from 'prcomp'. See <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/>
  and Sigg et al. (2008) <https://doi.org/10.1145/1390156.1390277> for more details.
authors:
- family-names: Sigg
  given-names: Christian
  email: christian@sigg-iten.ch
  orcid: https://orcid.org/0000-0003-1067-9224
preferred-citation:
  type: conference-paper
  title: Expectation-Maximization for Sparse and Non-Negative PCA
  authors:
  - family-names: Sigg
    given-names: Christian D.
  - family-names: Buhmann
    given-names: Joachim M.
  collection-title: Proc. 25th International Conference on Machine Learning
  collection-type: proceedings
  year: '2008'
  doi: 10.1145/1390156.1390277
  conference:
    name: Proc. 25th International Conference on Machine Learning
repository: https://chrsigg.r-universe.dev
repository-code: https://github.com/chrsigg/nsprcomp
commit: 036d20b2eb34f91348a0892aed0d9cc63c41a9c3
url: https://sigg-iten.ch/research/
contact:
- family-names: Sigg
  given-names: Christian
  email: christian@sigg-iten.ch
  orcid: https://orcid.org/0000-0003-1067-9224