# SHAPforxgboost 0.2.0
* 12/12/2025 Update to support xgboost 1.7.0 and above.

# SHAPforxgboost 0.1.2
* 17/05/2022 Added option `kind = "bar"` to `shap.plot.summary()`.

# SHAPforxgboost 0.1.1
* 22/03/2021 Moved `lightgbm` from imported to suggested package.

* 05/02/2021 Added `color_feature = "auto"` in `shap.plot.dependence` in order to 
colorize the (heuristically found) strongest interaction.

* 06/02/2021 Added vignette.

* 06/02/2021 Added function `shap.importance` to return mean absolute SHAP values per variable.

* 10/02/2021 Added `jitter_width`, `jitter_height` and `alpha` to `shap.plot.dependence`.

# SHAPforxgboost 0.1.0
* 12/12/2020 Added support for LightGBM.

# SHAPforxgboost 0.0.5
* Maintain documentations

# SHAPforxgboost 0.0.4
* 05/13/2020 fixed a problem in simple scatter plot, add `add_stat_cor` option

# SHAPforxgboost 0.0.3
* 01/22/2020 I fixed some issues raised, for example, y-axis goes under the data instead of on it.    
* 02/08/2020 Added an `var_cat` option to `shap.prep` so if supply a categorical variable, the long-format data would use `var_cat` as a labeling variable. For example, we can make two summary plot by adding `facet_wrap(~ var_cat)` to the `shap.plot.summary`  .
* Added an ID variable for each observation to the dataset produced by `shap.prep` and `shap.prep.stack.data`, which might be useful under certain situation.  
* Revised most functions' documentations.

# SHAPforxgboost 0.0.2
* Added a `NEWS.md` file to track changes to the package.
* 07/30/2019 Version 0.0.1 uploaded to cran.
* 08/10/2019 Version 0.0.2 released with update, fixed some bugs with dilute. 
* 08/27/2019 Major change, merged function `shap.plot.dependence.color` into `shap.plot.dependence`.
