Cannon A (2024). qrnn: Quantile Regression Neural Network. doi:10.32614/CRAN.package.qrnn, R package version 2.1.1, https://CRAN.R-project.org/package=qrnn.

Cannon AJ (2011). “Quantile regression neural networks: implementation in R and application to precipitation downscaling.” Computers & Geosciences, 37, 1277-1284. doi:10.1007/b98882.

Cannon AJ (2018). “Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes.” Stochastic Environmental Research and Risk Assessment, 32(11), 3207-3225. doi:10.1007/s00477-018-1573-6.

Corresponding BibTeX entries:

  @Manual{,
    title = {qrnn: Quantile Regression Neural Network},
    author = {Alex J. Cannon},
    year = {2024},
    note = {R package version 2.1.1},
    url = {https://CRAN.R-project.org/package=qrnn},
    doi = {10.32614/CRAN.package.qrnn},
  }

  @Article{,
    title = {Quantile regression neural networks: implementation in R
      and application to precipitation downscaling},
    author = {Alex J. Cannon},
    year = {2011},
    journal = {Computers \& Geosciences},
    volume = {37},
    pages = {1277-1284},
    doi = {10.1007/b98882},
  }

  @Article{,
    title = {Non-crossing nonlinear regression quantiles by monotone
      composite quantile regression neural network, with application to
      rainfall extremes},
    author = {Alex J. Cannon},
    year = {2018},
    journal = {Stochastic Environmental Research and Risk Assessment},
    volume = {32(11)},
    pages = {3207-3225},
    doi = {10.1007/s00477-018-1573-6},
  }