brulee 1.1.0

• 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).