Fitting Interpretable Neural Additive Models Using Orthogonalization R package ONAM version 1.0.1 An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established 'R' 'formula' notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>. Software Depends: keras3, reticulate Imports: dplyr, scales, rlang, ggplot2, pROC Suggests: akima, RColorBrewer, testthat (>= 3.0.0) David Köhler <koehler@imbie.uni-bonn.de> Comprehensive R Archive Network (CRAN) David Köhler [aut, cre] (ORCID: <https://orcid.org/0009-0006-0027-4046>) MIT + file LICENSE (https://CRAN.R-project.org/package=ONAM/LICENSE) 2026-01-26 application/tgz https://CRAN.R-project.org/package=ONAM doi:10.32614/CRAN.package.ONAM