Package: tabby 0.0.1

Max Kuhn

tabby: Tidy Interfaces for Tabular Deep Learning

Model definitions for fitting, tuning, and predicting with deep learning models for tabular data using the 'parsnip' and 'tidymodels' frameworks. Supported models include the 'TabPFN' foundation model of Hollmann et al. (2025) <doi:10.1038/s41586-024-08328-6>, 'TabICL' by Qu et al. (2025) <doi:10.48550/arXiv.2502.05564>, 'SAINT' by Somepalli et al. (2021) <doi:10.48550/arXiv.2106.01342>, and the tabular 'ResNet' of Gorishniy et al. (2021) <doi:10.48550/arXiv.2106.11959>, among others. Helper functions are also included to create space-filling grids for tuning parameters that are defined on a per-layer basis. These functions define the model interfaces; the computations are carried out by separate engine packages.

Authors:Max Kuhn [aut, cre], Edgar Ruiz [aut], Posit Software, PBC [cph, fnd]

tabby_0.0.1.tar.gz
tabby_0.0.1.zip(r-4.7-any)tabby_0.0.1.zip(r-4.6-any)
tabby_0.0.1.tar.gz(r-4.7-any)tabby_0.0.1.tar.gz(r-4.6-any)
tabby_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tabby/json (API)

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

Bug tracker:https://github.com/tidymodels/tabby/issues

Pkgdown/docs site:https://tabby.tidymodels.org

On CRAN:

Conda:

2.19 score 31 scripts 5 exports 38 dependencies

Last updated from:6985e3b8bc. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK157
source / vignettesOK235
linux-releaseOK147
windows-develOK122
windows-releaseOK109
wasm-releaseOK129

Exports:check_argsexpand_list_parametersmulti_predictneural_net_grid_space_fillingrequired_pkgs

Dependencies:clicodetoolscpp11dialsDiceDesigndplyrfarvergenericsggplot2globalsgluegtablehardhatisobandlabelinglifecyclemagrittrparsnippillarpkgconfigprettyunitspurrrR6RColorBrewerrlangS7scalessfdsparsevctrsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Model predictions across many sub-modelsmulti_predict._brulee_auto_int multi_predict._brulee_resnet multi_predict._brulee_rln
Working with Tuning Parameters that are Listsexpand_list_parameters neural_net_grid_space_filling