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Changelog

Source: NEWS.md

tidypredict (development version)

tidypredict 1.1.0

CRAN release: 2026-02-27

New Model Supports

  • Added support for rpart decision tree models (rpart). (#226)

  • Added support for CatBoost models (catboost.Model). (#179, #187, #188)

    • Objectives: RMSE, MAE, Quantile, MAPE, Poisson, Huber, LogCosh, Expectile, Tweedie, Logloss, CrossEntropy, MultiClass, and MultiClassOneVsAll.
    • Tree types: oblivious (default SymmetricTree) and non-oblivious (Depthwise or Lossguide grow policy).
    • Categorical features are handled automatically for parsnip/bonsai models; for raw CatBoost models use set_catboost_categories().

  • Added support for LightGBM models (lgb.Booster). (#177, #186)

    • Objectives: regression, binary classification, and multiclass classification.
    • Supports categorical features.
    • Supports linear trees (linear_tree = TRUE), which fit a linear model at each leaf instead of a constant.

Improvements

  • Tree models (rpart, partykit, ranger, randomForest, xgboost, lightgbm, catboost) now generate nested case_when() expressions that mirror the tree structure, instead of flat expressions with all leaf conditions at the same level. This produces more efficient SQL and R code because conditions are evaluated hierarchically. (#227)

  • parse_model() now documents the parsed model version system (v1/v2/v3) and model type classes in its help page. (#227)

  • earth() models now support additional GLM families and link functions: Gamma, inverse.gaussian, probit, and cloglog. (#194, #195)

  • glm() models now support additional families and link functions: Gamma family with inverse link, inverse.gaussian family with 1/mu^2 link, probit link, cloglog link, and sqrt link. (#203, #204, #205, #206, #207)

  • glmnet() models now support Gamma family and Cox proportional hazards (family = "cox") models. (#200, #201)

  • xgboost support now includes additional objectives: binary:hinge, reg:absoluteerror, reg:gamma, reg:pseudohubererror, and reg:squaredlogerror. (#184)

  • Added a vignette on floating-point precision issues with tree-based models. (#231)

Bug Fixes

  • tidypredict_fit() now correctly handles xgboost models with stump trees (single leaf, no splits). (#182)

  • tidypredict_fit() now correctly handles xgboost DART booster models with rate_drop > 0. DART uses tree weight normalization during training, and these weights are now properly applied to each tree’s predictions. (#183)

  • tidypredict_fit() now correctly incorporates base_score for xgboost models with count:poisson and reg:tweedie objectives. Previously, predictions were incorrect when base_score was not the default value. (#184)

  • tidypredict_fit() now correctly averages tree predictions for LightGBM models with boosting="rf" instead of summing them. (#185)

  • tidypredict_fit() now uses the correct split operator (<= instead of <) for ranger models. Previously, predictions were incorrect when data values exactly matched split values. (#189)

  • tidypredict_fit() now correctly averages tree predictions for ranger models instead of summing them. Previously, predictions were num.trees times too large. (#190)

  • tidypredict_fit() now throws a clear error for ranger and randomForest classification models, which are not supported. (#191, #193)

  • tidypredict_fit() now uses the correct split operator (<= instead of <) for randomForest models. (#192)

  • tidypredict_fit() now correctly handles partykit stump trees (models with no splits). (#196)

  • tidypredict_fit() now works with glmnet() models that use family function syntax (e.g., family = gaussian()) instead of string syntax (e.g., family = "gaussian"). (#197)

  • tidypredict_fit() now works with models that use family function syntax (e.g., family = gaussian()) instead of string syntax (e.g., family = "gaussian"). (#202)

tidypredict 1.0.1

CRAN release: 2025-12-13

Bug Fixes

  • Fixed bug where base_score wasn’t extracted correctly xgboost for version 3 or higher. (#173)

tidypredict 1.0.0

CRAN release: 2025-11-29

Breaking Changes

  • Random forest implementations (ranger and randomForest) will now produce a single formula instead of a list of expressions. (#84)

New Model Supports

  • Added support for glmnet models. (#165)

Improvements

  • xgboost models with objectives "reg:tweedie" and "count:poisson" are now supported. (#72, @SimonCoulombe)

  • tree based models now uses .default argument in produced case_when() code when applicable. (#153)

  • Speed up tidypredict_fit() for partykit and ranger packages. (#125)

  • Speed up tidypredict_fit() for xgboost models. (#130)

  • randomForest models now support regression outcomes. (#77)

  • An informative error will now be thrown if a lm model cannot be processed due to having linear combinations of predictors. (#124)

  • linear models such as lm() and glm() now work with interactions created with * and :. (#74)

  • Cubist rules will return simplified rules whenever possible to avoid multiplying by 0 and 1. (#152)

  • Make work with xgboost version > 2.0.0.0. (#169)

Bug Fixes

  • Fixed a bug where the intercept was added incorrectly to the result for cubist models. (#58)

  • Fixed bug where tidypredict would error on Cubist models without conditions. (#127)

  • Fixed bug where Cubst models incorrectly combined rules and committees. (#134)

tidypredict 0.5.1

CRAN release: 2024-12-19

  • Exported a number of internal functions to be used in {orbital} package

tidypredict 0.5

CRAN release: 2023-01-18

  • Changes maintainer to Edgar Ruiz

  • Updates author’s email addresses.

  • Removes dependency with stringr

  • Fixes issue with earth parsed_models (#108)

  • Addresses issues with XGBoost models

  • Improvements to XGBoosts tests

tidypredict 0.4.9

CRAN release: 2022-05-25

  • Fixes issue handling GLM Binomial earth models (#97)

  • Adds capability to handle single simple Cubist models (#57)

  • Fixed parenthesis issue in the creation of the interval formula (#76)

  • Fixed bug in SQL query generation for XGBoost models with objective binary:logistic.

  • Re-licensed package from GPL-3 to MIT. See consent from copyright holders here.

tidypredict 0.4.8

CRAN release: 2020-10-28

  • CRAN submission for a broken test case.

tidypredict 0.4.7

CRAN release: 2020-10-05

  • Change to with with version 5.1.2 and above of the earth package. As a result, tidypredict will only parse objects created by this and later versions of earth.

tidypredict 0.4.6

CRAN release: 2020-07-23

  • Small release for xgboost changes.

tidypredict 0.4.5

CRAN release: 2020-02-10

  • Switches maintainer to Max Kuhn

tidypredict 0.4.3

CRAN release: 2019-09-03

  • Adds support for categorical predictors in partykit

  • Fixes parsnip tests to meet standards of new CRAN version

tidypredict 0.4.2

CRAN release: 2019-07-15

  • Simplifies tests that verify ranger

  • Adds fit method for parsed xgboost models

  • Sets conditional requirement for xgboost, for test and vignette

tidypredict 0.4.0

CRAN release: 2019-07-12

New features

  • Parses ranger classification models.

  • Adds method support for broom’s tidy() function. Regression models only

  • Adds as_parsed_model() function. It adds the proper class components to the list.

  • Adds initial support for partykit’s ctree() model

  • Adds support for parsnip fitted models: lm, randomForest, ranger, and earth

  • Adds support for xgb.Booster models provided by the xgboost package (@Athospd, #43)

  • Adds support for Cubist::cubist() models (# 36)

tidypredict 0.3.0

CRAN release: 2019-01-10

New features

  • Adds support for MARS models provided by the earth package

Improvements

  • New parsed models are now list objects as opposed to data frames.

  • tidypredict_to_column() no longer supports ranger and randomForest because of the multiple queries generated by multiple trees.

  • All functions that read the parsed models and create the tidy eval formula now use the list object.

  • Most of the code that depends on dplyr programming has been removed.

  • Removes dependencies on: tidyr, tibble

  • The x/y interface for earth models can now be used.

Bug Fixes

  • It now returns all of the trees instead of just one for tree based models (randomForest & ranger) (#29)

tidypredict 0.2.1

CRAN release: 2018-12-20

Bug Fixes

  • tibble 2.0.0 compatibility fix (@krlmlr)

tidypredict 0.2.0

CRAN release: 2018-02-25

New features

Bug fixes