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# CITATION.cff file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "specProc" in publications use:'
type: software
license: MIT
title: 'specProc: Preprocessing Tools for Laser-Induced Breakdown Spectroscopy'
version: 0.8.3.9000
abstract: 'Tools for exploring, preprocessing and modeling spectroscopic data, developed
  for laser-induced breakdown spectroscopy (LIBS) and also applicable to other techniques
  such as Raman, infrared, and inductively coupled plasma optical emission spectroscopy.
  Covers the workflow from raw spectra to calibrated results: peak finding and fitting,
  wavelength calibration, baseline correction, normalization and scaling, orthogonal
  signal correction including orthogonal partial least squares (OPLS; Trygg and Wold
  (2002) <https://doi.org/10.1002/cem.695>), wavelength selection (variable importance
  in projection, selectivity ratio, interval partial least squares), calibration transfer,
  calibration curves and figures of merit, and plasma diagnostics (Boltzmann and Saha-Boltzmann
  plots, Stark broadening). Includes robust tools for outlier detection and exploration:
  robust principal component analysis (Hubert, Rousseeuw and Vanden Branden (2005)
  <https://doi.org/10.1198/004017004000000563>), robust partial least squares regression
  (Hubert and Vanden Branden (2003) <https://doi.org/10.1002/cem.822>) and principal
  component regression (Hubert and Verboven (2003) <https://doi.org/10.1002/cem.783>)
  with their robust cross-validation (Engelen and Hubert (2005) <https://doi.org/10.1016/j.aca.2005.01.015>),
  robust classification by SIMCA (Vanden Branden and Hubert (2005) <https://doi.org/10.1016/j.chemolab.2005.03.002>)
  and discriminant analysis (Hubert and Van Driessen (2004) <https://doi.org/10.1016/S0167-9473(02)00299-2>),
  detection of deviating cells (Rousseeuw and Van den Bossche (2018) <https://doi.org/10.1080/00401706.2017.1340909>),
  PCA with cellwise outliers and missing values (MacroPCA; Hubert, Rousseeuw and Van
  den Bossche (2019) <https://doi.org/10.1080/00401706.2018.1562989>), robust transformations
  to central normality (Raymaekers and Rousseeuw (2021) <https://doi.org/10.1007/s10994-021-05960-5>),
  and self-organizing maps (Kohonen (1982) <https://doi.org/10.1007/BF00337288>).
  Preprocessing steps are also available as ''recipes'' steps, and the robust models
  as ''parsnip'' models and engines, for ''tidymodels'' workflows. Computationally
  intensive steps are implemented in ''C++'' via ''Rcpp'' and ''RcppEigen''.'
authors:
- family-names: Goueguel
  given-names: Christian L.
  email: christian.goueguel@gmail.com
  orcid: https://orcid.org/0000-0003-0521-3446
preferred-citation:
  type: manual
  title: 'specProc: Preprocessing Tools for Laser-Induced Breakdown Spectroscopy'
  authors:
  - family-names: Goueguel
    given-names: Christian L.
    orcid: https://orcid.org/0000-0003-0521-3446
    email: christian.goueguel@gmail.com
  year: '2026'
  notes: R package version 0.8.3.9000
  url: https://github.com/ChristianGoueguel/specProc
repository: https://christiangoueguel.r-universe.dev
repository-code: https://github.com/ChristianGoueguel/specProc
commit: 03cdf40dcb19f69c587da34925f27c869aa3beaa
url: https://christiangoueguel.com/specProc/
date-released: '2026-10-05'
contact:
- family-names: Goueguel
  given-names: Christian L.
  email: christian.goueguel@gmail.com
  orcid: https://orcid.org/0000-0003-0521-3446