ONAM
1.1.0Fitting Interpretable Neural Additive Models Using Orthogonalization
Overview
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.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 97%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Repository
Repository practices
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/koehlibert/onam_r on 2026-08-23.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- 1.1.0Latest
- RR 4.6.0 released · 2026-04-24
- 1.0.12026-01-26 · diff ↗
- 1.0.02025-11-11
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-11-11
- Total releases
- 3 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 29 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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