Add rpart support

DESCRIPTION

@@ -10,7 +10,7 @@ Description: It parses a fitted 'R' model object, and returns a formula in
     'Tidy Eval' code that calculates the predictions.  It works with
     several databases back-ends because it leverages 'dplyr' and 'dbplyr'
     for the final 'SQL' translation of the algorithm. It currently
-    supports lm(), glm(), randomForest(), ranger(), earth(),
+    supports lm(), glm(), randomForest(), ranger(), rpart(), earth(),
     xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and
     ctree() models.
 License: MIT + file LICENSE
@@ -46,6 +46,7 @@ Suggests:
     partykit,
     randomForest,
     ranger,
+    rpart,
     rmarkdown,
     RSQLite,
     survival,

NAMESPACE

@@ -15,6 +15,7 @@ S3method(parse_model,model_fit)
 S3method(parse_model,party)
 S3method(parse_model,randomForest)
 S3method(parse_model,ranger)
+S3method(parse_model,rpart)
 S3method(parse_model,xgb.Booster)
 S3method(print,tidypredict_test)
 S3method(tidy,pm_regression)
@@ -35,6 +36,7 @@ S3method(tidypredict_fit,pm_tree)
 S3method(tidypredict_fit,pm_xgb)
 S3method(tidypredict_fit,randomForest)
 S3method(tidypredict_fit,ranger)
+S3method(tidypredict_fit,rpart)
 S3method(tidypredict_fit,xgb.Booster)
 S3method(tidypredict_interval,data.frame)
 S3method(tidypredict_interval,glm)
@@ -47,6 +49,7 @@ S3method(tidypredict_test,glmnet)
 S3method(tidypredict_test,lgb.Booster)
 S3method(tidypredict_test,model_fit)
 S3method(tidypredict_test,party)
+S3method(tidypredict_test,rpart)
 S3method(tidypredict_test,xgb.Booster)
 export(.build_case_when_tree)
 export(.build_linear_pred)
@@ -57,6 +60,7 @@ export(.extract_lgb_trees)
 export(.extract_partykit_classprob)
 export(.extract_ranger_classprob)
 export(.extract_rf_classprob)
+export(.extract_rpart_classprob)
 export(.extract_xgb_trees)
 export(acceptable_formula)
 export(as_parsed_model)

NEWS.md

@@ -2,6 +2,8 @@
 
 ## New Model Supports
 
+- Added support for rpart decision tree models (`rpart`). (#226)
+
 - Added support for CatBoost models (`catboost.Model`). (#TBD, #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).