Package: neuralGAM 1.1.1

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.

Authors:Ines Ortega-Fernandez [aut, cre, cph], Marta Sestelo [aut, cph]

neuralGAM_1.1.1.tar.gz
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neuralGAM_1.1.1.tgz(r-4.4-any)neuralGAM_1.1.1.tgz(r-4.3-any)
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neuralGAM.pdf |neuralGAM.html
neuralGAM/json (API)
NEWS

# Install 'neuralGAM' in R:
install.packages('neuralGAM', repos = c('https://inesortega.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/inesortega/neuralgam/issues

On CRAN:

deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai

5.20 score 2 stars 34 scripts 16k downloads 3 exports 54 dependencies

Last updated 2 months agofrom:46b93fb945. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 13 2024
R-4.5-winOKOct 13 2024
R-4.5-linuxOKOct 13 2024
R-4.4-winOKOct 13 2024
R-4.4-macOKOct 13 2024
R-4.3-winOKOct 13 2024
R-4.3-macOKOct 13 2024

Exports:autoplotinstall_neuralGAMneuralGAM

Dependencies:backportsbase64encclicolorspaceconfigfansifarverformula.toolsgenericsggplot2gluegridExtragtablehereisobandjsonlitekeraslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeoperator.toolspillarpkgconfigpngprocessxpsR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootrstudioapiscalestensorflowtfautographtfrunstibbletidyselectutf8vctrsviridisLitewhiskerwithryamlzeallot