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Citing

If scikit-opls supports published work, please cite both the software and the methods it implements.

Software

Machine-readable metadata lives in CITATION.cff; GitHub renders a formatted citation from it under Cite this repository.

@software{scikit_opls,
  author  = {Madsen, Jakob S.},
  title   = {scikit-opls: Orthogonal Projections to Latent Structures for scikit-learn},
  url     = {https://github.com/HauserGroup/scikit-opls},
  version = {0.1.0}
}

Methods

OPLS and the orthogonal filter:

  • Trygg, J. & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119–128. https://doi.org/10.1002/cem.695
  • Wold, S., Antti, H., Lindgren, F. & Öhman, J. (1998). Orthogonal signal correction of near-infrared spectra. Chemometrics and Intelligent Laboratory Systems, 44(1–2), 175–185. https://doi.org/10.1016/S0169-7439(98)00109-9

O2PLS:

  • Trygg, J. & Wold, S. (2003). O2-PLS, a two-block (X–Y) latent variable regression (LVR) method with an integral OSC filter. Journal of Chemometrics, 17(1), 53–64. https://doi.org/10.1002/cem.775

OPLS-DA:

  • Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E. & Trygg, J. (2006). OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20(8–10), 341–351. https://doi.org/10.1002/cem.1006

VIP scores:

  • Galindo-Prieto, B., Eriksson, L. & Trygg, J. (2014). Variable influence on projection (VIP) for OPLS models. Journal of Chemometrics, 28(8), 623–632. https://doi.org/10.1002/cem.2627