tidypredict 0.4.6

• Small release for xgboost changes.

tidypredict 0.4.5

• Switches maintainer to Max Kuhn

tidypredict 0.4.3

• Adds support for categorical predictors in partykit

• Fixes parsnip tests to meet standards of new CRAN version

tidypredict 0.4.2

• 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

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

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

Bug Fixes

• tibble 2.0.0 compatibility fix (@krlmlr)

tidypredict 0.2.0

New features

• Add support for ranger() models.

Bug fixes

• Using x ~. in a randomForest() formula fails (#18 @washcycle).