tabpfn 0.3.0
Added quantile regression support to
predict().tab_pfn()’sversionargument now accepts bare version numbers in addition to"v"-prefixed strings. A"v"is prepended automatically, soversion = 2.5,version = "2.5", andversion = "v2.5"are all equivalent.New
tabpfn_initialize()eagerly loads the Pythontabpfnlibrary (and PyTorch). Call it right afterlibrary(tabpfn)and before other OpenMP-using packages to avoid the segmentation fault described in #34.When the
"r-tabpfn"environment (created byinstall_tabpfn()) is the Python installation reticulate resolves to, thetabpfnPython 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 aversionargument to pin a specifictabpfnrelease and, by default, offers to upgrade an existing environment when a newer release is available.Added a
typeargument to be consistent with parsnip. Defaults toNULL, which will produce all prediction types.The fitted
tab_pfnobject now records:The underlying TabPFN model version in a
versionelement, 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 adeviceelement, 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 aversionargument to select a specific TabPFN model version (e.g.,"v2","v2.5"). WhenNULL, the Python library’s current default is used (#15).New
tabpfn_list_versions()returns the model versions supported by the currently installed Pythontabpfnlibrary.Added
tabpfn_download_models()to close (#15) @frankiethull
