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qrnn (version 2.0.5)

Quantile Regression Neural Network

Description

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) and Cannon (2018) .

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Version

Install

install.packages('qrnn')

Monthly Downloads

676

Version

2.0.5

License

GPL-2

Maintainer

Alex Cannon

Last Published

September 13th, 2019

Functions in qrnn (2.0.5)

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