Prior-Data Fitted Network Foundational Model for Tabular Data
Overview
Provides a consistent API for classification and regression models based on the 'TabPFN' model of Hollmann et al. (2025), "Accurate predictions on small data with a tabular foundation model," Nature, 637(8045) doi:10.1038/s41586-024-08328-6. The calculations are served via 'Python' to train and predict the model.
- Documented how to pin a version of one of the Python
tabpfnlibrary's own dependencies, such astorchornumpy, when a new release of that dependency breaks atabpfnversion that worked before. Useextra_packagesininstall_tabpfn()for the persistent environment, orreticulate::py_require()for the ephemeral one. See the "Pinning dependencies" section in?install_tabpfn(#40). - The
training_set_limitargument oftab_pfn()now defaults toInf, so all of your data is used. It previously sampled anything larger down to 10,000 rows without saying so. tab_pfn()no longer checks your data against the model's limits; the Python library does, and its error names the model in use. Limits now follow the version you chose rather than one fixed set.- Fixed a failure that stopped models from fitting at all on some setups, reported as
AttributeError: property 'stream' of '_StderrHandler' object has no setter(#40). The package no longer usesreticulate::py_capture_output(), which reassigns the streams of Python's logging handlers and errors when one of them is read-only. - Failures coming from the Python library are now reported as R errors. The message keeps what the library said and drops the wrapper around it, and
tab_pfn()adds what to do about it in R, such as loweringtraining_set_limit. This coverspredict()as well. ?tab_pfnnow lists each model version's limits, including the lower one that applies on a CPU, and its examples use current versions rather than earlier ones.- Test snapshots now record TabPFN v3.5, the model the Python library currently defaults to. They will need updating again whenever that default moves.
- CI pins
torchto 2.13.0. The 2.14.0 release triggers the_StderrHandlerfailure above, and the pin stays until that is settled upstream.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-03-19
Documentation
- Help pages
- 10 (2 internal)includes a package overview page
- Examples that run
- 43%3 run in a plain check (1 of them under a condition) · 1 only when donttest examples are enabled (R CMD check --as-cran) · 3 never run
- Exported functions with examples
- 75%
- Documented parameters
- 100%
- Return-value docs
- 88%
- Help pages citing references
- 20%
Downloads
Repository
- PRs merged (all time)
- 94%
- Median days to close, 50 most recently updated closed issues
- 8
- Median days to close, 50 most recently updated closed PRs
- 1
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9 development-tooling and community-health practices detected across 5 families in the upstream repository
Checks run against github.com/tidymodels/tabpfn on 2026-10-18.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-09-25 · The repository has a site deploy workflow
pkgdown site last built 2026-09-25
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Dependencies
Installing tabpfn adds 26 packages (17 with compiled code, 11 MB of source tarballs). Heaviest: Rcpp 1.8 MB, reticulate 1.6 MB, jsonlite 1.0 MB.
Code & Tests
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Package metadata
- First published
- 2026-03-18
- Total releases
- 4 / 1 yrs
- License
- Apache License (>= 2)
- Minimum R
- ≥ 4.1.0
- Download size
- 220 KB
Cite
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Run in R for the authors' preferred citation:
citation("tabpfn")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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