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CaDENCE
View on CRAN: Click
here
Download and install CaDENCE package within the R console
Install from CRAN:
install.packages("CaDENCE")
Install from Github:
library("remotes")
install_github("cran/CaDENCE") Install by package version:
library("remotes")
install_version("CaDENCE", "1.2.5") Attach the package and use:
library("CaDENCE")
Maintained by
Alex J. Cannon
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-02-09
Latest Update: 2017-12-05
Description:
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, etc. following Cannon (2012) .
How to cite:
Alex J. Cannon (2013). CaDENCE: Conditional Density Estimation Network Construction and Evaluation. R package version 1.2.5, https://cran.r-project.org/web/packages/CaDENCE. Accessed 04 Jun. 2026.
Previous versions and publish date:
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Other R packages that CaDENCE depends,
imports, suggests or enhances
Complete documentation for CaDENCE
Functions, R codes and Examples using
the CaDENCE R package
Some associated functions: CaDENCE-package . FraserSediment . bgamma . blnorm . bweibull . cadence.cost . cadence.fit . cadence.initialize . cadence.predict . dummy.code . gam.style . logistic . pareto2 . rbf . rprop . xval.buffer .
Some associated R codes: cadence.cost.R . cadence.evaluate.R . cadence.fit.R . cadence.initialize.R . cadence.predict.R . cadence.reshape.R . dbgamma.R . dblnorm.R . dbpareto2.R . dbweibull.R . dpareto2.R . dummy.code.R . gam.style.R . logistic.R . pbgamma.R . pblnorm.R . pbpareto2.R . pbweibull.R . ppareto2.R . qbgamma.R . qblnorm.R . qbpareto2.R . qbweibull.R . qpareto2.R . rbf.R . rbgamma.R . rblnorm.R . rbpareto2.R . rbweibull.R . rpareto2.R . rprop.R . xval.buffer.R . Full CaDENCE package functions and examples
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