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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 canada.ca> |
| 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: 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.