Changelog

Source: NEWS.md

orbital 0.5.0

CRAN release: 2026-02-27

New models

• orbital() now works with boost_tree(engine = "catboost") models for numeric, class, and probability predictions. (#90)

• orbital() now works with boost_tree(engine = "lightgbm") models for numeric, class, and probability predictions. (#89)

• orbital() now works with decision_tree(engine = "rpart") models for numeric, class, and probability predictions. (#128)

• orbital() now works with mars(engine = "earth") models for class and probability predictions. (#127)

• orbital() now works with multinom_reg(engine = "glmnet") models for class and probability predictions. (#127)

• orbital() now works with rand_forest(engine = "randomForest") models for class and probability predictions. (#127)

• orbital() now works with rand_forest(engine = "ranger") models for class and probability predictions. (#127)

Improvements

• orbital() gains a separate_trees argument for tree ensemble models (xgboost, lightgbm, catboost, ranger, randomForest). When TRUE, each tree is emitted as a separate intermediate column before being summed, which can enable parallel evaluation in columnar databases like DuckDB, Snowflake, and BigQuery. For models with many trees, the final summation is automatically batched in groups of 50 to avoid expression depth limits in databases. See the “Separate trees” vignette for details. (#105)

• Added support for step_spline_b(), step_spline_convex(), step_spline_monotone(), step_spline_natural(), and step_spline_nonnegative() from the recipes package. (#99)

• step_YeoJohnson() is now supported. (#96)

• Binary classification probability predictions now generate cleaner code by having the second probability reference the first (e.g., .pred_1 = 1 - .pred_0) instead of duplicating the full expression. (#100)

• New “Database deployment” vignette shows how to deploy predictions to a database as tables or views. (#74)

• New “SQL size” vignette documents how model type and hyperparameters affect generated SQL size, and shows how to jointly tune for predictive performance and SQL complexity.

Bug fixes

• All numeric values embedded in SQL expressions now use full IEEE 754 double precision (17 significant digits) to ensure exact round-trip accuracy between R and database predictions. This prevents subtle numerical drift in regularized model coefficients, normalized features, and tree split values. (#138)

orbital 0.4.1

CRAN release: 2025-12-13

• Make work with new versions of xgboost. (#119)

orbital 0.4.0

CRAN release: 2025-12-04

• Added support for tailor package and its integration into workflows. The following adjustments have gained orbital() support. (#103)

  • adjust_equivocal_zone()
  • adjust_numeric_range()
  • adjust_predictions_custom()
  • adjust_probability_threshold()

• Added show_query() method for orbital objects. (#106)

• Fixed printing bug where output would get malformed if coefficients had similarities. (#115)

orbital 0.3.1

CRAN release: 2025-08-30

• Fixed bug where PCA steps didn’t work if they were trained with more than 99 predictors. (#82)

• step_pca_sparse() no longer generate code with terms with 0 in them. (#51)

• Fixed bugs in all PCA steps where an error occurred depending on which predictors were selected. (#52)

• Fixed bug where large PCA results wouldn’t work with data bases. (#84)

orbital 0.3.0

CRAN release: 2024-12-22

• orbital() has gained type argument to change prediction type. (#66)

• orbital() now works with logistic_reg(engine = "glm") models for class prediction and probability predictions. (#62, #66)

• orbital() now works with boost_tree(engine = "xgboost") models for class prediction and probability predictions. (#71)

• orbital() now works with decision_tree(engine = "partykit") models for class prediction and probability predictions. (#77)

• augment() method for orbital() object have been added. (#55)

• orbital() gained prefix argument to allow for renaming of prediction columns. (#59)

orbital 0.2.0

CRAN release: 2024-07-28

• Support for step_dummy(), step_impute_mean(), step_impute_median(), step_impute_mode(), step_unknown(), step_novel(), step_other(), step_BoxCox(), step_inverse(), step_mutate(), step_sqrt(), step_indicate_na(), step_range(), step_intercept(), step_ratio(), step_lag(), step_log(), step_rename() has been added. (#17)

• Support for step_upsample(), step_smote(), step_smotenc(), step_bsmote(), step_adasyn(), step_rose(), step_downsample(), step_nearmiss(), and step_tomek() has been added. (#21)

• Support for step_bin2factor(), step_discretize(), step_lencode_mixed(), step_lencode_glm(), step_lencode_bayes() has been added. (#22)

• Support for step_pca_sparse(), step_pca_sparse_bayes() and step_pca_truncated() as been added. (#23)

• orbital() now works on tune::last_fit() objects. (#13)

• orbital_predict() has been removed and replaced with the more idiomatic predict() method. (#10)

orbital 0.1.0

CRAN release: 2024-07-01

• Initial CRAN submission.