To cite package 'gamboostLSS' in publications use:

B. Hofner, A. Mayr, N. Fenske and M. Schmid (2018). gamboostLSS: Boosting Methods for GAMLSS Models, R package version 2.0-1, https://CRAN.R-project.org/package=gamboostLSS.

To cite the package and the tutorial in publications use:

Benjamin Hofner, Andreas Mayr, Matthias Schmid (2016). gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework. Journal of Statistical Software, 74(1), 1-31.<doi:10.18637/jss.v074.i01>

To cite the theory of 'gamboostLSS' use:

Mayr, A., N. Fenske, B. Hofner, T. Kneib and M. Schmid (2012). Generalized additive models for location, scale and shape for high-dimensional data - a flexible approach based on boosting. Journal of the Royal Statistical Society, Series C - Applied Statistics, 61(3): 403-427.

To cite the noncyclical fitting method of 'gamboostLSS' use:

Thomas, J., Mayr, A., Bischl, B., Schmid, M., Smith, A., and Hofner, B. (2018). Gradient boosting for distributional regression - faster tuning and improved variable selection via noncyclical updates. Statistics and Computing. 28(3): 673-687. DOI 10.1007/s11222-017-9754-6

Use ‘toBibtex(citation("gamboostLSS"))’ to extract BibTeX references.

Corresponding BibTeX entries:

  @Manual{,
    title = {{gamboostLSS}: Boosting Methods for {GAMLSS} Models},
    author = {Benjamin Hofner and Andreas Mayr and Nora Fenske and
      Matthias Schmid},
    year = {2018},
    note = {{R} package version 2.0-1},
    url = {https://CRAN.R-project.org/package=gamboostLSS},
  }
  @Article{,
    title = {{gamboostLSS}: An {R} Package for Model Building and
      Variable Selection in the {GAMLSS} Framework},
    author = {Benjamin Hofner and Andreas Mayr and Matthias Schmid},
    journal = {Journal of Statistical Software},
    year = {2016},
    volume = {74},
    number = {1},
    pages = {1--31},
    doi = {10.18637/jss.v074.i01},
  }
  @Article{,
    title = {Generalized additive models for location, scale and shape
      for high-dimensional data - a flexible approach based on
      boosting},
    author = {Andreas Mayr and Nora Fenske and Benjamin Hofner and
      Thomas Kneib and Matthias Schmid},
    journal = {Journal of the Royal Statistical Society, Series C -
      Applied Statistics},
    year = {2012},
    volume = {61},
    number = {3},
    pages = {403--427},
  }
  @Article{,
    title = {Gradient boosting for distributional regression - faster
      tuning and improved variable selection via noncyclical updates},
    author = {Janek Thomas and Andreas Mayr and Bernd Bischl and
      Matthias Schmid and Adam Smith and Benjamin Hofner},
    year = {2018},
    journal = {{Statistics and Computing}},
    volume = {28},
    number = {3},
    pages = {673--687},
    doi = {10.1007/s11222-017-9754-6},
  }