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
DOI: 10.32614/CRAN.package.CaDENCE
Author: Alex J. Cannon
Maintainer: Alex J. Cannon <alex.cannon at>
License: GPL-2
NeedsCompilation: no
Citation: CaDENCE citation info
In views: Distributions
CRAN checks: CaDENCE results


Reference manual: CaDENCE.pdf


Package source: CaDENCE_1.2.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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