
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(), rpart(), earth(), xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and ctree() models.
Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co
Authors:
Edgar Ruiz edgar@posit.co
Max Kuhn max@posit.co
Useful links: