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Changelog

Source: NEWS.md

tabpfn 0.4.0

  • Documented how to pin a version of one of the Python tabpfn library’s own dependencies, such as torch or numpy, when a new release of that dependency breaks a tabpfn version that worked before. Use extra_packages in install_tabpfn() for the persistent environment, or reticulate::py_require() for the ephemeral one. See the “Pinning dependencies” section in ?install_tabpfn (#40).

  • The training_set_limit argument of tab_pfn() now defaults to Inf, 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 uses reticulate::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 lowering training_set_limit. This covers predict() as well.

  • ?tab_pfn now 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 torch to 2.13.0. The 2.14.0 release triggers the _StderrHandler failure above, and the pin stays until that is settled upstream.

tabpfn 0.3.0

CRAN release: 2026-09-01

  • Added quantile regression support to predict().

  • tab_pfn()’s version argument now accepts bare version numbers in addition to "v"-prefixed strings. A "v" is prepended automatically, so version = 2.5, version = "2.5", and version = "v2.5" are all equivalent.

  • New tabpfn_initialize() eagerly loads the Python tabpfn library (and PyTorch). Call it right after library(tabpfn) and before other OpenMP-using packages to avoid the segmentation fault described in #34.

  • When the "r-tabpfn" environment (created by install_tabpfn()) is the Python installation reticulate resolves to, the tabpfn Python library is now imported eagerly at load time so that PyTorch claims OpenMP before other packages can, avoiding a segmentation fault (#34).

  • New install_tabpfn() sets up a persistent "r-tabpfn" Python virtual environment. It has a version argument to pin a specific tabpfn release and, by default, offers to upgrade an existing environment when a newer release is available.

  • Added a type argument to be consistent with parsnip. Defaults to NULL, which will produce all prediction types.

  • The fitted tab_pfn object now records:

  • The underlying TabPFN model version in a version element, which is also shown by the print method. It falls back to "unknown" if the version cannot be determined.

  • The device(s) used to fit the model (e.g. "cpu", "mps", or "cuda:0") in a device element, which is also shown by the print method. It falls back to "unknown" if the device cannot be determined.

tabpfn 0.2.0

CRAN release: 2026-05-14

  • Updated notes on License Requirements in ?tab_pfn.

  • control_tab_pfn() now accepts ... to pass additional arguments directly to the TabPFN Python constructor (e.g. model_path).

  • tab_pfn() gains a version argument to select a specific TabPFN model version (e.g., "v2", "v2.5"). When NULL, the Python library’s current default is used (#15).

  • New tabpfn_list_versions() returns the model versions supported by the currently installed Python tabpfn library.

  • Added tabpfn_download_models() to close (#15) @frankiethull

tabpfn 0.1.0

CRAN release: 2026-03-18

  • Initial version