CaDENCE: Conditional Density Estimation Network Construction and Evaluation

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, and the like.

Install the latest version of this package by entering the following in R:
install.packages("CaDENCE")
AuthorAlex J. Cannon
Date of publication2017-03-23 21:05:02 UTC
MaintainerAlex J. Cannon <alex.cannon@canada.ca>
LicenseGPL-2
Version1.2.4

View on CRAN

Functions

bgamma Man page
blnorm Man page
bpareto2 Man page
bweibull Man page
CaDENCE Man page
cadence.cost Man page
cadence.evaluate Man page
cadence.fit Man page
cadence.initialize Man page
CaDENCE-package Man page
cadence.predict Man page
cadence.reshape Man page
dbgamma Man page
dblnorm Man page
dbpareto2 Man page
dbweibull Man page
dpareto2 Man page
dummy.code Man page
FraserSediment Man page
gam.style Man page
logistic Man page
pareto2 Man page
pbgamma Man page
pblnorm Man page
pbpareto2 Man page
pbweibull Man page
ppareto2 Man page
qbgamma Man page
qblnorm Man page
qbpareto2 Man page
qbweibull Man page
qpareto2 Man page
rbf Man page
rbgamma Man page
rblnorm Man page
rbpareto2 Man page
rbweibull Man page
rpareto2 Man page
rprop Man page
xval.buffer Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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