# qrnn v2.0.5

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## Quantile Regression Neural Network

Fit quantile regression neural network models with optional
left censoring, partial monotonicity constraints, generalized additive
model constraints, and the ability to fit multiple non-crossing quantile
functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.

## Functions in qrnn

Name | Description | |

dummy.code | Convert a factor to a matrix of dummy codes | |

gam.style | Modified generalized additive model plots for interpreting QRNN models | |

qrnn2 | Fit and make predictions from QRNN models with two hidden layers | |

qrnn.initialize | Initialize a QRNN weight vector | |

transfer | Transfer functions and their derivatives | |

tilted.abs | Tilted absolute value function | |

quantile.dtn | Interpolated quantile distribution with exponential tails | |

YVRprecip | Daily precipitation data at Vancouver Int'l Airport (YVR) | |

censored.mean | A hybrid mean/median function for left censored variables | |

composite.stack | Reformat data matrices for composite quantile regression | |

adam | Adaptive stochastic gradient descent optimization algorithm (Adam) | |

qrnn.predict | Evaluate quantiles from trained QRNN model | |

qrnn.cost | Smooth approximation to the tilted absolute value cost function | |

qrnn.fit | Main function used to fit a QRNN model or ensemble of QRNN models | |

qrnn-package | Quantile Regression Neural Network | |

qrnn.rbf | Radial basis function kernel | |

huber | Huber norm and Huber approximations to the ramp and tilted absolute value functions | |

mcqrnn | Monotone composite quantile regression neural network (MCQRNN) for simultaneous estimation of multiple non-crossing quantiles | |

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## Details

Type | Package |

License | GPL-2 |

LazyLoad | yes |

Repository | CRAN |

NeedsCompilation | no |

Packaged | 2019-09-12 21:29:20 UTC; ECPACIFIC+CannonA |

Date/Publication | 2019-09-13 05:10:02 UTC |

Contributors |

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