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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[](http://www.rdocumentation.org/packages/qrnn)