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)