Run Predictions Inside the Database
| Package name | Version | Title | Date | Size | License | |
|---|---|---|---|---|---|---|
| tidypredict | 1.0.0 | Run Predictions Inside the Database | Sat Nov 29 2025 | 238.46kB | MIT + file LICENSE | |
| tidypredict | 0.5.1 | Run Predictions Inside the Database | Thu Dec 19 2024 | 1336.59kB | MIT + file LICENSE | |
| tidypredict | 0.5 | Run Predictions Inside the Database | Wed Jan 18 2023 | 979.46kB | MIT + file LICENSE | |
| tidypredict | 0.4.9 | Run Predictions Inside the Database | Wed May 25 2022 | 980.20kB | MIT + file LICENSE | |
| tidypredict | 0.4.8 | Run Predictions Inside the Database | Wed Oct 28 2020 | 970.86kB | GPL-3 | |
| tidypredict | 0.4.7 | Run Predictions Inside the Database | Mon Oct 05 2020 | 963.05kB | GPL-3 | |
| tidypredict | 0.4.6 | Run Predictions Inside the Database | Thu Jul 23 2020 | 962.12kB | GPL-3 | |
| tidypredict | 0.4.5 | Run Predictions Inside the Database | Mon Feb 10 2020 | 957.03kB | GPL-3 | |
| tidypredict | 0.4.4 | Run Predictions Inside the Database | Sun Feb 09 2020 | 957.01kB | GPL-3 | |
| tidypredict | 0.4.3 | Run Predictions Inside the Database | Tue Sep 03 2019 | 957.14kB | GPL-3 | |
| tidypredict | 0.4.2 | Run Predictions Inside the Database | Mon Jul 15 2019 | 953.87kB | GPL-3 | |
| tidypredict | 0.4.1 | Run Predictions Inside the Database | Sun Jul 14 2019 | 953.98kB | GPL-3 | |
| tidypredict | 0.4.0 | Run Predictions Inside the Database | Fri Jul 12 2019 | 953.66kB | GPL-3 | |
| tidypredict | 0.3.0 | Run Predictions Inside the Database | Thu Jan 10 2019 | 428.13kB | GPL-3 | |
| tidypredict | 0.2.1 | Run Predictions Inside the Database | Thu Dec 20 2018 | 36.59kB | GPL-3 | |
| tidypredict | 0.2.0 | Run Predictions Inside the Database | Sun Feb 25 2018 | 36.18kB | GPL-3 | |
| tidypredict | 0.1.0 | Run Predictions Inside the Database | Wed Jan 17 2018 | 32.88kB | GPL-3 |
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(), xgb.Booster.complete(), cubist(), and ctree() models.
Useful links