tidypredict: Run Predictions Inside the Database (original) (raw)

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:	0.5.1
Depends:	R (≥ 3.6)
Imports:	cli, dplyr (≥ 0.7), generics, knitr, purrr, rlang (≥ 1.1.1), tibble, tidyr
Suggests:	covr, Cubist, DBI, dbplyr, earth (≥ 5.1.2), methods, mlbench, modeldata, nycflights13, parsnip, partykit, randomForest, ranger, rmarkdown, RSQLite, testthat (≥ 3.2.0), xgboost, yaml
Published:	2024-12-19
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
URL:	https://tidypredict.tidymodels.org,https://github.com/tidymodels/tidypredict
NeedsCompilation:	no
Materials:	README, NEWS
In views:	ModelDeployment
CRAN checks:	tidypredict results

Documentation:

Reference manual:	tidypredict.html , <tidypredict.pdf>
Vignettes:	Cubist models (source, R code) Generalized Linear Regression (source, R code) Linear Regression (source, R code) MARS models via the 'earth' package (source, R code) Non-R Models (source, R code) Random Forest, using Ranger (source, R code) Create a regression spec - version 2 (source, R code) Random Forest (source, R code) Save and re-load models (source, R code) Database write-back (source, R code) Create a tree spec - version 2 (source, R code) XGBoost models (source, R code)

Reference manual:

tidypredict.html , <tidypredict.pdf>

Vignettes:

Cubist models (source, R code) Generalized Linear Regression (source, R code) Linear Regression (source, R code) MARS models via the 'earth' package (source, R code) Non-R Models (source, R code) Random Forest, using Ranger (source, R code) Create a regression spec - version 2 (source, R code) Random Forest (source, R code) Save and re-load models (source, R code) Database write-back (source, R code) Create a tree spec - version 2 (source, R code) XGBoost models (source, R code)

Downloads:

Reverse dependencies:

Linking:

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