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.
| Version: |
1.1.0 |
| Depends: |
R (≥ 3.6) |
| Imports: |
cli, dplyr (≥ 0.7), generics, jsonlite, knitr, purrr, rlang (≥ 1.1.1), tibble, tidyr |
| Suggests: |
bonsai, covr, Cubist (≥ 0.5.1), DBI, dbplyr, earth (≥
5.1.2), glmnet, lightgbm, methods, mlbench, modeldata, nycflights13, parsnip, partykit, randomForest, ranger (≥
0.14.1), rpart (≥ 4.1.0), rmarkdown, RSQLite, survival, testthat (≥ 3.2.0), withr, xgboost, yaml |
| Published: |
2026-02-27 |
| DOI: |
10.32614/CRAN.package.tidypredict |
| Author: |
Emil Hvitfeldt [aut, cre],
Edgar Ruiz [aut],
Max Kuhn [aut] |
| Maintainer: |
Emil Hvitfeldt <emil.hvitfeldt at posit.co> |
| BugReports: |
https://github.com/tidymodels/tidypredict/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://tidypredict.tidymodels.org,
https://github.com/tidymodels/tidypredict |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| In views: |
ModelDeployment |
| CRAN checks: |
tidypredict results |