neuralGAM: Interpretable Neural Network Based on Generalized Additive Models

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: 1.1.0
Imports: tensorflow, keras, ggplot2, magrittr, reticulate, formula.tools, gridExtra
Suggests: covr, testthat (≥ 3.0.0), fs, withr
Published: 2023-09-20
Author: Ines Ortega-Fernandez ORCID iD [aut, cre, cph], Marta Sestelo ORCID iD [aut, cph]
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 (>= 3.8), keras, tensorflow
Materials: README NEWS
CRAN checks: neuralGAM results

Documentation:

Reference manual: neuralGAM.pdf

Downloads:

Package source: neuralGAM_1.1.0.tar.gz
Windows binaries: r-devel: neuralGAM_1.1.0.zip, r-release: neuralGAM_1.1.0.zip, r-oldrel: neuralGAM_1.1.0.zip
macOS binaries: r-release (arm64): neuralGAM_1.1.0.tgz, r-oldrel (arm64): neuralGAM_1.1.0.tgz, r-release (x86_64): neuralGAM_1.1.0.tgz
Old sources: neuralGAM archive

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