# 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).
