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An implementation of the 'Evidential Neural Network for Regression' model recently introduced in Denoeux (2023) <doi:10.1109/TFUZZ.2023.3268200>. In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in Denoeux (2023) <doi:10.1016/j.fss.2022.06.004>. The package contains functions for training the network, tuning hyperparameters by cross-validation or the hold-out method, and making predictions. It also contains utilities for making calculations with Gaussian random fuzzy numbers (such as, e.g., computing the degrees of belief and plausibility of an interval, or combining Gaussian random fuzzy numbers).
Version: | 1.1.1 |
Depends: | R (≥ 3.1.0) |
Imports: | evclust, stats |
Suggests: | knitr, rmarkdown, nnet, MASS, ggplot2 |
Published: | 2024-05-09 |
DOI: | 10.32614/CRAN.package.evreg |
Author: | Thierry Denoeux |
Maintainer: | Thierry Denoeux <tdenoeux at utc.fr> |
License: | GPL-3 |
NeedsCompilation: | no |
Materials: | NEWS |
In views: | MachineLearning |
CRAN checks: | evreg results |
Reference manual: | evreg.pdf |
Vignettes: |
Introduction to the evreg package |
Package source: | evreg_1.1.1.tar.gz |
Windows binaries: | r-devel: evreg_1.1.1.zip, r-release: evreg_1.1.1.zip, r-oldrel: evreg_1.1.1.zip |
macOS binaries: | r-release (arm64): evreg_1.1.1.tgz, r-oldrel (arm64): evreg_1.1.1.tgz, r-release (x86_64): evreg_1.1.1.tgz, r-oldrel (x86_64): evreg_1.1.1.tgz |
Old sources: | evreg 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.