tidypredict: Run Predictions Inside the Database
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.
| Version: |
0.4.9 |
| Depends: |
R (≥ 3.1) |
| Imports: |
dplyr (≥ 0.7), generics, knitr, purrr, rlang, stringr, tibble, tidyr |
| Suggests: |
covr, Cubist, DBI, dbplyr, earth (≥ 5.1.2), methods, mlbench, modeldata, nycflights13, parsnip, partykit, randomForest, ranger, rmarkdown, RSQLite, testthat (≥ 3.0.0), xgboost, yaml |
| Published: |
2022-05-25 |
| Author: |
Max Kuhn [aut, cre],
Edgar Ruiz [aut] |
| Maintainer: |
Max Kuhn <max at rstudio.com> |
| 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 |
Documentation:
Downloads:
Reverse dependencies:
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