Package {tabpfn}

Title:	Prior-Data Fitted Network Foundational Model for Tabular Data
Version:	0.3.0
Description:	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.
License:	Apache License (≥ 2)
URL:	https://tabpfn.tidymodels.org, https://github.com/tidymodels/tabpfn
BugReports:	https://github.com/tidymodels/tabpfn/issues
Depends:	R (≥ 4.1.0)
Imports:	cli, dplyr, generics, hardhat (≥ 1.4.1), jsonlite, purrr, reticulate (≥ 1.41.0.1), rlang (≥ 1.1.0), tibble
Suggests:	covr, ggplot2, MASS, modeldata, recipes, rstudioapi, spelling, testthat (≥ 3.0.0), withr
Config/Needs/website:	tidyverse/tidytemplate
Config/testthat/edition:	3
Encoding:	UTF-8
Language:	en-US
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-09-01 22:17:52 UTC; max
Author:	Max Kuhn [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer:	Max Kuhn <max@posit.co>
Repository:	CRAN
Date/Publication:	2026-09-01 22:50:02 UTC

tabpfn: Prior-Data Fitted Network Foundational Model for Tabular Data

Description

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.

Author(s)

Maintainer: Max Kuhn max@posit.co (ORCID)

Authors:

• Max Kuhn max@posit.co (ORCID)

Other contributors:

• Posit Software, PBC (ROR) [copyright holder, funder]

See Also

Useful links:

• https://tabpfn.tidymodels.org

• https://github.com/tidymodels/tabpfn

• Report bugs at https://github.com/tidymodels/tabpfn/issues

Controlling TabPFN execution

Description

Controlling TabPFN execution

Usage

control_tab_pfn(
  n_preprocessing_jobs = 1L,
  device = "auto",
  ignore_pretraining_limits = FALSE,
  inference_precision = "auto",
  fit_mode = "fit_preprocessors",
  memory_saving_mode = "auto",
  random_state = sample.int(10^6, 1),
  ...
)

Arguments

Value

A list with extra class "control_tab_pfn" that has named elements for each of the argument values.

References

https://github.com/PriorLabs/TabPFN/blob/main/src/tabpfn/classifier.py, https://github.com/PriorLabs/TabPFN/blob/main/src/tabpfn/regressor.py

Examples

control_tab_pfn()

Install the TabPFN Python environment

Description

Sets up a persistent Python virtual environment containing the tabpfn Python library so that tab_pfn() and friends can use it. By default the environment is named "r-tabpfn", which reticulate automatically discovers and prefers over an ephemeral environment (see the Environment discovery section).

Usage

install_tabpfn(
  version = "default",
  envname = "r-tabpfn",
  check_latest = TRUE,
  extra_packages = NULL,
  python_version = NULL,
  method = c("auto", "virtualenv", "conda"),
  new_env = identical(envname, "r-tabpfn"),
  restart_session = TRUE,
  ...
)

Arguments

Value

Invisibly returns the environment name.

Environment discovery

Because the package calls reticulate::import("tabpfn"), reticulate will automatically use a virtual environment named "r-tabpfn" if one exists, preferring it over the ephemeral environment that is otherwise created on demand. Environments selected via RETICULATE_PYTHON, VIRTUAL_ENV, or a project-local .venv take precedence over "r-tabpfn".

Examples

## Not run: 
# Install the latest release into "r-tabpfn"
install_tabpfn()

# Pin a specific version
install_tabpfn(version = "2.0.9")

## End(Not run)

Check the Python package installation

Description

Attempts to import the Python package

Usage

is_tab_pfn_installed()

Value

A single logical

Examples

if (interactive()) {
 # This may take a minute
 is_tab_pfn_installed()
}

Predict using TabPFN

Description

Predict using TabPFN

Usage

## S3 method for class 'tab_pfn'
predict(object, new_data, type = NULL, quantile_levels = NULL, ...)

## S3 method for class 'tab_pfn'
augment(x, new_data, type = NULL, quantile_levels = NULL, ...)

Arguments

Value

predict() returns a tibble of predictions and augment() appends the columns in new_data. In either case, the number of rows in the tibble is guaranteed to be the same as the number of rows in new_data.

For regression data, the prediction is in the column .pred. For classification, the class predictions are in .pred_class and the probability estimates are in columns with the pattern ⁠.pred_{level}⁠ where level is the levels of the outcome factor vector.

When quantile_levels is given, regression results also have a .pred_quantile column of hardhat::quantile_pred() values.

Examples

## Not run: 
if (rlang::is_installed(c("MASS", "ggplot2")) &
     is_tab_pfn_installed() &
     interactive()) {
  library(ggplot2)

  motorcycles <- MASS::mcycle
  in_tr <- seq(1, nrow(motorcycles), by = 2)
  mcycle_tr <- motorcycles[in_tr, ]
  mcycle_te <- motorcycles[-in_tr, ]

  mcycle_grid <-
   dplyr::tibble(
     times = seq(min(motorcycles$times), max(motorcycles$times), length.out = 200)
   )
  mcycle_grid$.row <- seq_len(nrow(mcycle_grid))

  fit <- tab_pfn(accel ~ times, data = mcycle_tr)

  # ------------------------------------------------------------------------------
  # Predict mean acceleration

  mean_pred <- augment(fit, mcycle_grid)

  mean_p <-
   mean_pred |>
   ggplot(aes(times)) +
   geom_point(data = mcycle_te, aes(y = accel), alpha = 1 / 2) +
   geom_line(aes(y = .pred))

  #------------------------------------------------------------------------------Predict 5 %, 50%
  # Predict 5%, 50%, and 90% quantiles of acceleration

  q_pred <-
   predict(fit,
           mcycle_grid,
           type = "quantile",
           quantile_levels = c(0.1, 0.5, 0.9))
  q_pred$.row <- seq_len(nrow(q_pred))

  q_pred_longer <-
   q_pred$.pred_quantile |>
   dplyr::as_tibble() |>
   dplyr::full_join(mcycle_grid, by = ".row") |>
   dplyr::mutate(level = format(.quantile_levels))

  mean_p +
   geom_line(
     data = q_pred_longer,
     aes(y = .pred_quantile, col = level, group = level)
   )
}

## End(Not run)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

augment()

Fit a TabPFN model.

Description

tab_pfn() applies data to a pre-estimated deep learning model defined by Hollmann et al (2025). This model emulates Bayesian inference for regression and classification models.

Usage

tab_pfn(x, ...)

## Default S3 method:
tab_pfn(x, ...)

## S3 method for class 'data.frame'
tab_pfn(
  x,
  y,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  version = NULL,
  control = control_tab_pfn(),
  ...
)

## S3 method for class 'matrix'
tab_pfn(
  x,
  y,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  version = NULL,
  control = control_tab_pfn(),
  ...
)

## S3 method for class 'formula'
tab_pfn(
  formula,
  data,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  version = NULL,
  control = control_tab_pfn(),
  ...
)

## S3 method for class 'recipe'
tab_pfn(
  x,
  data,
  num_estimators = 8L,
  softmax_temperature = 0.9,
  balance_probabilities = FALSE,
  average_before_softmax = FALSE,
  training_set_limit = 10000,
  version = NULL,
  control = control_tab_pfn(),
  ...
)

Arguments

Details

Computing Requirements

This model can be used with or without a graphics processing unit (GPU). However, it is fairly limited when used with a CPU (and no GPU). There might be additional data size limitation warnings with CPU computations, and, understandably, the execution time is much longer. CPU computations can also consume a significant amount of system memory, depending on the size of your data.

GPUs using CUDA (Compute Unified Device Architecture) are most effective. Limited testing with others has shown that GPUs with Metal Performance Shaders (MPS) instructions (e.g., Apple GPUs) have limited utility for these specific computations and might be slower than the CPU for some data sets.

License Requirements

Starting with version 2.5, using TabPFN requires accepting the model license and obtaining a token from PriorLabs. Each model version (v2.5, v2.6, etc.) has its own license that must be accepted individually.

To set up access:

1. Visit https://platform.priorlabs.ai:443/ and create an account.

2. Go to the License tab and accept the license for each model version you intend to use.

3. Obtain your token from your account page.

4. Set the TABPFN_TOKEN environment variable. The easiest way is to add it to your .Renviron file:

TABPFN_TOKEN=your_token_value

The usethis function edit_r_environ() can be very helpful here.

Users who already have TABPFN_TOKEN set can use TabPFN v2 without any additional steps.

Python Installation

You will need a working Python virtual environment with the correct packages to use these modeling functions.

There are at least two ways to proceed.

Ephemeral uv Install

The first approach, which we strongly suggest, is to simply load this package and attempt to run a model. This will prompt reticulate to create an ephemeral environment and automatically install the required packages. That process would look like this:

  > library(tabpfn)
  >
  > predictors <- mtcars[, -1]
  > outcome <- mtcars[, 1]
  >
  > # XY interface
  > mod <- tab_pfn(predictors, outcome)
  Downloading uv...Done!
  Downloading cpython-3.12.12 (download) (15.9MiB)
   Downloading cpython-3.12.12 (download)
  Downloading setuptools (1.1MiB)
  Downloading scikit-learn (8.2MiB)
  Downloading numpy (4.9MiB)

  <downloading and installing more packages>

   Downloading llvmlite
   Downloading torch
  Installed 58 packages in 350ms
  > mod
  TabPFN Regression Model

  Training set
  i 32 data points
  i 10 predictors

The location of the environment can be found at tools::R_user_dir("reticulate", "cache").

See the documentation for reticulate::py_require() to learn more about this method.

Persistent Environment with install_tabpfn()

Alternatively, install_tabpfn() creates a persistent virtual environment named "r-tabpfn" and installs the Python tabpfn library into it:

  library(tabpfn)

  # Install the latest release
  install_tabpfn()

  # Or pin a specific version for reproducibility
  install_tabpfn(version = "2.0.9")

You do not need to call use_virtualenv() afterwards: because this package imports the Python module "tabpfn", reticulate automatically discovers and prefers the "r-tabpfn" environment over the ephemeral one. Run install_tabpfn() before tabpfn has initialized Python (i.e., before fitting a model); if Python is already loaded, restart R first.

Data

Be default, there are limits to the training data dimensions:

• Version 2.0: number of training set samples (10,000) and, the number of predictors (500). There is an unchangeable limit to the number of classes (10).

• Version 2.5: number of training set samples (50,000) and, the number of predictors (2,000). There is an unchangeable limit to the number of classes (10).

Predictors do not require preprocessing; missing values and factor vectors are allowed.

Model Selection

By default, TabPFN uses the Python library's current default model version. There are two ways to override this.

Selecting a model version

Use the version argument to select a specific released model version. For example:

  # Use version 2.0
  mod <- tab_pfn(predictors, outcome, version = "v2")

  # Use version 2.5
  mod <- tab_pfn(predictors, outcome, version = "v2.5")

Pointing to a local model file

If you have a model file on disk (e.g., downloaded for offline use), pass its path via control_tab_pfn(model_path = ...):

  ctrl <- control_tab_pfn(model_path = "/path/to/model_file.ckpt")
  mod  <- tab_pfn(predictors, outcome, control = ctrl)

Note that version and model_path are mutually exclusive: if version is set, it overwrites any model_path supplied through control.

Calculations

For the softmax_temperature value, the softmax terms are:

exp(value / softmax_temperature)

A value of softmax_temperature = 1 results in a plain softmax value.

Value

A tab_pfn object with elements:

• fit: the python object containing the model.

• levels: a character string of class levels (or NULL for regression)

• training: a vector with the training set dimensions.

• version: the underlying TabPFN model version (or "unknown" if it cannot be determined).

• device: the device(s) the model was fitted on, e.g. "cpu", "mps", or "cuda:0" (or "unknown" if it cannot be determined).

• logging: any R or python messages produced by the computations.

• blueprint: am object produced by hardhat::mold() used to process new data during prediction.

References

Hollmann, Noah, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, and Frank Hutter. "Accurate predictions on small data with a tabular foundation model." Nature 637, no. 8045 (2025): 319-326.

Hollmann, Noah, Samuel Müller, Katharina Eggensperger, and Frank Hutter. "Tabpfn: A transformer that solves small tabular classification problems in a second." arXiv preprint arXiv:2207.01848 (2022).

Müller, Samuel, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter. "Transformers can do Bayesian inference." arXiv preprint arXiv:2112.10510 (2021).

See Also

control_tab_pfn(), predict.tab_pfn()

Examples

predictors <- mtcars[, -1]
outcome <- mtcars[, 1]

## Not run: 
if (is_tab_pfn_installed() & interactive()) {
 # XY interface
 mod <- tab_pfn(predictors, outcome)

 # Formula interface
 mod2 <- tab_pfn(mpg ~ ., mtcars)

 # Recipes interface
 if (rlang::is_installed("recipes")) {
  suppressPackageStartupMessages(library(recipes))
  rec <-
   recipe(mpg ~ ., mtcars) %>%
   step_log(disp)

  mod3 <- tab_pfn(rec, mtcars)
  mod3
 }
}

## End(Not run)

Download all TabPFN pre-trained model checkpoints

Description

As of 2026-05-05, there are 36 pre-trained models equaling roughly 1.2 GB of storage. Each model is trained on various synthetic & real datasets tailored to classification & regression. This function routine will require you to sign a one-time license for both 2.5 & 2.6 model varieties. Downloading all models will take some time.

Usage

tabpfn_download_models(cache_dir = NULL)

Arguments

Value

Invisibly returns NULL. Called for its side effect of downloading model files.

Examples

tabpfn_download_models()

Eagerly initialize the TabPFN Python library

Description

Forces the Python tabpfn library (and its PyTorch dependency) to load now instead of on first use. Because PyTorch bundles its own OpenMP runtime, loading it before any other package that uses OpenMP avoids the segmentation fault described in https://github.com/tidymodels/tabpfn/issues/34.

For this to work, call it as the very first thing in your session, using tabpfn::tabpfn_initialize() (with the :: prefix so it runs before library(tabpfn) and before any other package that might load OpenMP, such as recipes):

tabpfn::tabpfn_initialize()
library(tabpfn)
suppressPackageStartupMessages(library(recipes))
fit_obj <- tab_pfn(mpg ~ ., data = mtcars)

Usage

tabpfn_initialize()

Value

NULL, invisibly. Called for its side effect of loading the Python library.

Examples

## Not run: 
tabpfn::tabpfn_initialize()
library(tabpfn)

## End(Not run)

List available TabPFN model versions

Description

Returns a character vector of valid model version strings accepted by tab_pfn()'s version argument. The available model versions are queried directly from the currently installed Python tabpfn library, not hard-coded in this package, so results may differ across Python library versions.

Usage

tabpfn_list_versions()

Value

A character vector of model version strings.