Package: neuralGAM Type: Package Title: Interpretable Neural Network Based on Generalized Additive Models Version: 2.0.1 Authors@R: c(person("Ines", "Ortega-Fernandez", role = c("aut", "cre", "cph"), email = "iortega@gradiant.org", comment = c(ORCID = "0000-0002-8041-6860")), person("Marta", "Sestelo", role = c("aut","cph"), email = "sestelo@uvigo.es", comment = c(ORCID = "0000-0003-4284-6509")) ) Maintainer: Ines Ortega-Fernandez Description: Neural Additive Model 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. License: MPL-2.0 BugReports: https://github.com/inesortega/neuralGAM/issues Encoding: UTF-8 Imports: tensorflow, keras, ggplot2, magrittr, reticulate, formula.tools, matrixStats, patchwork, rlang SystemRequirements: python (>= 3.10), keras (== 2.15), tensorflow (== 2.15) RoxygenNote: 7.3.3 Roxygen: list(markdown = TRUE) Suggests: covr, testthat (>= 3.0.0), fs, withr Config/testthat/edition: 3 URL: https://inesortega.github.io/neuralGAM/, https://github.com/inesortega/neuralGAM Config/pak/sysreqs: libpng-dev python3 Repository: https://inesortega.r-universe.dev Date/Publication: 2026-07-01 22:15:03 UTC RemoteUrl: https://github.com/inesortega/neuralgam RemoteRef: HEAD RemoteSha: 52b6d82d49a7ce18cb41f931217480c59b266366 NeedsCompilation: no Packaged: 2026-07-01 23:21:26 UTC; root Author: Ines Ortega-Fernandez [aut, cre, cph] (ORCID: ), Marta Sestelo [aut, cph] (ORCID: )