To cite package 'FDboost' itself use the manual (Brockhaus and Ruegamer 2018) and the tutorial (Brockhaus et al. 2017a); to cite functional linear array models use Brockhaus et al. (2015); to cite models with historical effects use Brockhaus et al. (2017b); to cite models with factor-specific historical effects use Ruegamer (2018).

Brockhaus, S. and Ruegamer, D. (2018), FDboost: Boosting Functional Regression Models, R package version 0.3-1

Brockhaus, S., Ruegamer, D., and Greven, S. (2017a), Boosting Functional Regression Models with FDboost, ArXiv e-prints 1705.10662.

Brockhaus, S., Scheipl, F., Hothorn, T., and Greven, S. (2015), The Functional Linear Array Model. Statistical Modelling, 15(3), 279-300.

Brockhaus, S., Melcher, M., Leisch, F., and Greven, S. (2017b), Boosting flexible functional regression models with a high number of functional historical effects. Statistics and Computing, 27(4), 913-926.

Ruegamer D., Brockhaus, S., Gentsch K., Scherer, K., Greven, S. (2018). Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, 621-642.

Corresponding BibTeX entries:

  @Manual{,
    title = {FDboost: Boosting Functional Regression Models},
    author = {Sarah Brockhaus and David Ruegamer},
    year = {2018},
  }

  @Article{,
    title = {Boosting Functional Regression Models with FDboost},
    author = {Sarah Brockhaus and David Ruegamer and Sonja Greven},
    journal = {ArXiv e-prints},
    year = {2017},
    eprint = {1705.10662},
    month = {may},
  }

  @Article{,
    title = {The Functional Linear Array Model},
    author = {Sarah Brockhaus and Fabian Scheipl and Torsten Hothorn
      and Sonja Greven},
    journal = {Statistical Modelling},
    year = {2015},
    volume = {15},
    number = {3},
    pages = {279--300},
  }

  @Article{,
    author = {Sarah Brockhaus and Michael Melcher and Friedrich Leisch
      and Sonja Greven},
    title = {Boosting flexible functional regression models with a high
      number of functional historical effects},
    journal = {Statistics and Computing},
    year = {2017},
    volume = {27},
    number = {4},
    pages = {913--926},
  }

  @Article{,
    author = {David Ruegamer and Sarah Brockhaus and Kornelia Gentsch
      and Klaus Scherer and Sonja Greven},
    title = {Boosting factor-specific functional historical models for
      the detection of synchronization in bioelectrical signals},
    journal = {Journal of the Royal Statistical Society: Series C
      (Applied Statistics)},
    eprint = {1609.06070},
    year = {2018},
    volume = {67},
    pages = {621--642},
  }