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Changelog

Source: NEWS.md

brulee 1.2.0

  • predict() for regression brulee_tab_icl() models gained two new type values. "quantile" returns a .pred_quantile column (a hardhat::quantile_pred() vector) at the levels given by the new predict-time quantile_levels argument, which defaults to (1:9) / 10. "variance" returns the variance of the predictive distribution in a .pred_variance column. The TabICL regression head was already a quantile regression head internally, so this exposes a distribution the model was always computing; type = "numeric" is unchanged and remains the default. Unlike brulee_chronos(), the levels are not fixed when the model is created and any value in the open interval (0, 1) can be requested. See ?predict.brulee_tab_icl for how ensemble members are pooled for each type.

  • Added augment() methods for all brulee model fits.

    • For regression models, .pred and .resid are added. For the latter, it is computed when the outcome is present in the data. See the exceptions below for foundational models.
    • For classification, the hard class predictions and class probability estimates are added.
    • For brulee_chronos() models, the forecast columns are aligned to the rows of new_data rather than to the internal per-series prediction order.
    • For brulee_tab_icl() regression models, quantile regression estimates and the prediction variance can be obtained by setting the quantile_levels argument to a non-null value. .resid is then measured against the median of the predictive distribution.

  • The error thrown when predict() is given an unsupported type is now attributed to predict() rather than to brulee’s internal helper.

  • predict() now clamps an epoch larger than the number of epochs actually fit, which is what its documentation and its warning have always promised. Previously it warned and then failed with a subscript out of bounds error, and an epoch exactly equal to length(fit$estimates) failed with no warning at all. Because early stopping makes the number of epochs fit vary by platform, a fixed epoch could work on one machine and fail on another. coef() was already correct and now shares the same check (#138).

brulee 1.1.1

CRAN release: 2026-07-13

  • Pretrained model weights (for brulee_tab_icl() and brulee_chronos()) are no longer downloaded automatically when the package is attached. When the weights are missing, both brulee_tab_icl() and brulee_chronos() now prompt to download them in an interactive session and error otherwise. TabICL weights can also be downloaded explicitly with tab_icl_download_weights() (#130).

  • Downloaded model weights are now cached in the per-user cache directory returned by tools::R_user_dir("brulee", "cache"), which respects platform conventions, rather than under ~/.cache (#130).

  • brulee_resnet() no longer returns all-NA predictions when the training-set size leaves a single-row trailing batch (e.g. mtcars with default settings). Such a batch made batch-normalization compute a variance over one sample, corrupting its buffers with NaN (#122).

brulee 1.1.0

CRAN release: 2026-07-02

  • brulee_tab_icl() makes the open-source foundational model TabICL available. On first use, there is a substantial download (~ 400MB) for the model weights that is cached locally.

  • brulee_saint() and brulee_auto_int() now support gradient clipping via the grad_value_clip and grad_norm_clip arguments (both default to 5), matching brulee_mlp() and brulee_resnet(). This prevents the loss from overflowing to NaN during training with aggressive learning rates.

  • There is now a type argument to predict.brulee_chronos(): "all" returns .pred and .pred_quantile (unchanged default), "numeric" returns only .pred, "quantile" returns only .pred_quantile. The id column is still prepended for multi-series models regardless of type.

  • Fixed a bug where torch’s L-BFGS optimizers internal convergence flag is NA, throwing an unhelpful error.

Breaking Changes

  • The brulee_saint() argument use_target_token was renamed to target_token.

  • predict() for brulee_chronos() models was reworked. The historical context is always the data supplied to brulee_chronos() (the model is pretrained and does no training), so the former new_data context-override was removed. The argument previously called future_df is now new_data: it describes the future window to forecast for and may have at most prediction_length rows per series (previously exactly prediction_length). When fewer rows are supplied, the forecast is truncated to those rows. predict() also gained a type argument ("all", "numeric", or "quantile") to select which prediction columns are returned.

  • All estimated models now include epoch zero (the randomly initialized parameters, before any training) as the first element of loss and estimates, matching the neural-network models. These vectors are now length epochs + 1, epoch = 0 is a valid argument to predict() and coef(), and the entry for best_epoch is at position best_epoch + 1. Predictions and coefficients for a given (positive) epoch are unchanged. Note: objects serialized by earlier versions of these three functions predict off by one epoch under the new indexing, so refit any stored models.

    • The print() methods now report the loss from the best epoch. Previously the displayed loss was taken one epoch too early (it ignored the prepended epoch-zero entry in loss).

brulee 1.0.0

CRAN release: 2026-06-17

New models for tabular data:

  • Regularization Learning Networks (brulee_rln()) use a conventional MLP architecture but each weight learns its own adaptive regularization coefficient.

  • ResNet (brulee_resnet()) can fit a multilayer neural network with skip (i.e. residual) connections and batch normalization.

  • AutoInt (brulee_auto_int()) uses residual connections and columnwise attention mechanisms to create embeddings that encourage in-context learning of features.

  • Saint (brulee_saint()) uses column and/or row attention mechanisms.

  • Chronos2 (brulee_chronos()) is a foundational model for forecasting.

  • All modeling functions now support GPU acceleration via the device parameter. Users can specify device = "cpu", device = "cuda", or device = "mps" (Apple Silicon). When device = NULL (default), the package automatically selects CUDA if available, otherwise defaults to CPU. Note: MPS is not auto-selected because it doesn’t support float64 dtype required by brulee. See?training_efficiency for some related notes.

Breaking Changes

  • Float tensors were changed from 64-bit floats to 32-bit. This is to enable GPU usage on MPS devices.

  • Parameters are initialized on CPU devices and then converted to the chosen device. In some cases, the RNG initialization code is independent of the seed.

  • For classification, the softmax was moved out of every model’s forward pass so the loss can use torch::nnf_cross_entropy() (which applies the log-sum-exp trick internally) instead of nll_loss(log(softmax(x))). This avoids log(0) underflow that produced NaN losses and “numerical overflow” early stopping on overspecified brulee_saint() / brulee_auto_int() fits. Affects brulee_mlp(), brulee_logistic_reg(), brulee_multinomial_reg(), brulee_resnet(), brulee_auto_int(), and brulee_saint(). New fits carry output_type = "logits" so the predict path applies softmax; serialized fits from earlier versions of brulee continue to predict correctly.

brulee 0.6.0

CRAN release: 2025-09-02

  • Transition from the magrittr pipe to the base R pipe.

  • To try to help avoiding numeric overflow in the loss functions:

    • Tensors are stored as a 64-bit float instead of 32-bit.

    • Starting values were transitioned to using Gaussian distribution (instead of uniform) with a smaller standard deviation.

    • The results always contain the initial results to use as a fallback if there is overflow during the first epoch.

    • brulee_mlp() has two additional parameters, grad_value_clip and grad_value_clip, that prevent issues.

    • The warning was changed to “Early stopping occurred at epoch {X} due to numerical overflow of the loss function.”

  • Several new SGD optimizers were added: "ADAMw", "Adadelta", "Adagrad", and "RMSprop".

  • Mixture parameter values different than zero cannot be used for several optimizers since they require L2 penalties.

brulee 0.5.0

CRAN release: 2025-04-07

  • Removed a unit test for numerical overflow since it occurs less frequently and has become increasingly more challenging to reproduce.

brulee 0.4.0

CRAN release: 2025-01-30

  • Added a convenience function, brulee_mlp_two_layer(), to more easily fit two-layer networks with parsnip.

  • Various changes and improvements to error and warning messages.

  • Fixed a bug that occurred when linear activation was used for neural networks (#68).

brulee 0.3.0

CRAN release: 2024-02-14

  • Fixed bug where coef() didn’t would error if used on a brulee_logistic_reg() that was trained with a recipe. (#66)

  • Fixed a bug where SGD always being used as the optimizer (#61).

  • Additional activation functions were added (#74).

brulee 0.2.0

CRAN release: 2022-09-19

  • Several learning rate schedulers were added to the modeling functions (#12).

  • An optimizer was added to [brulee_mlp()], with a new default being LBFGS instead of stochastic gradient descent.

brulee 0.1.0

CRAN release: 2022-02-02

  • Modeling functions gained a mixture argument for the proportion of L1 penalty that is used. (#50)

  • Penalization was not occurring when quasi-Newton optimization was chosen. (#50)

brulee 0.0.1

CRAN release: 2021-12-15

First CRAN release.