Package: ONAM 1.0.1

ONAM: Fitting Interpretable Neural Additive Models Using Orthogonalization

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>.

Authors:David Köhler [aut, cre]

ONAM_1.0.1.tar.gz
ONAM_1.0.1.zip(r-4.7)ONAM_1.0.1.zip(r-4.6)ONAM_1.0.1.zip(r-4.5)
ONAM_1.0.1.tgz(r-4.6-any)ONAM_1.0.1.tgz(r-4.5-any)
ONAM_1.0.1.tar.gz(r-4.7-any)ONAM_1.0.1.tar.gz(r-4.6-any)
ONAM_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ONAM/json (API)

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

Bug tracker:https://github.com/koehlibert/onam_r/issues

On CRAN:

Conda:

deep-learninginterpretable-machine-learningmachine-learningstatistical-learning

3.82 score 12 scripts 476 downloads 5 exports 51 dependencies

Last updated from:44c94acbbb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK154
source / vignettesOK175
linux-release-x86_64OK172
macos-release-arm64OK182
macos-oldrel-arm64OK179
windows-develOK100
windows-releaseOK94
windows-oldrelOK91
wasm-releaseOK96

Exports:decomposeinstall_conda_envonamplot_inter_effectplot_main_effect

Dependencies:backportsbase64enccliconfigcpp11dottydplyrfarverfastmapgenericsggplot2gluegtablehereisobandjsonlitekeras3labelinglatticelifecyclemagrittrMatrixpillarpkgconfigpngpROCprocessxpsR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootrstudioapiS7scalestensorflowtfautographtfrunstibbletidyselectutf8vctrsviridisLitewhiskerwithryamlzeallot