The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Version: | 1.1.1 |
Imports: | tensorflow, keras, ggplot2, magrittr, reticulate, formula.tools, gridExtra |
Suggests: | covr, testthat (≥ 3.0.0), fs, withr |
Published: | 2024-04-19 |
DOI: | 10.32614/CRAN.package.neuralGAM |
Author: | Ines Ortega-Fernandez
|
Maintainer: | Ines Ortega-Fernandez <iortega at gradiant.org> |
BugReports: | https://github.com/inesortega/neuralGAM/issues |
License: | MPL-2.0 |
URL: | https://inesortega.github.io/neuralGAM/, https://github.com/inesortega/neuralGAM |
NeedsCompilation: | no |
SystemRequirements: | python (>= 3.10), keras, tensorflow |
Materials: | README NEWS |
CRAN checks: | neuralGAM results |
Reference manual: | neuralGAM.pdf |
Package source: | neuralGAM_1.1.1.tar.gz |
Windows binaries: | r-devel: neuralGAM_1.1.1.zip, r-release: neuralGAM_1.1.1.zip, r-oldrel: neuralGAM_1.1.1.zip |
macOS binaries: | r-release (arm64): neuralGAM_1.1.1.tgz, r-oldrel (arm64): neuralGAM_1.1.1.tgz, r-release (x86_64): neuralGAM_1.1.1.tgz, r-oldrel (x86_64): neuralGAM_1.1.1.tgz |
Old sources: | neuralGAM archive |
Please use the canonical form https://CRAN.R-project.org/package=neuralGAM to link to this page.
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.
Health stats visible at Monitor.