Fitting Interpretable Neural Additive Models Using Orthogonalization
Set up conda environment for keras functionality
Get variance decomposition of orthogonal neural additive model
Fit orthogonal neural additive model
Plot Interaction Effect
Plot Main Effect
Evaluate orthogonal neural additive model
Get summary of an onam object
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>.