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, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

Version:	1.2.5
Depends:	pso
Suggests:	boot
Published:	2017-12-05
Author:	Alex J. Cannon
Maintainer:	Alex J. Cannon <alex.cannon at canada.ca>
License:	GPL-2
NeedsCompilation:	no
Citation:	CaDENCE citation info
In views:	Distributions
CRAN checks:	CaDENCE results

Documentation:

Reference manual:

CaDENCE.pdf

Downloads:

Package source:	CaDENCE_1.2.5.tar.gz
Windows binaries:	r-devel: CaDENCE_1.2.5.zip, r-release: CaDENCE_1.2.5.zip, r-oldrel: CaDENCE_1.2.5.zip
macOS binaries:	r-release (arm64): CaDENCE_1.2.5.tgz, r-oldrel (arm64): CaDENCE_1.2.5.tgz, r-release (x86_64): CaDENCE_1.2.5.tgz, r-oldrel (x86_64): CaDENCE_1.2.5.tgz
Old sources:	CaDENCE archive

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