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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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

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