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:	0.5
Depends:	R (≥ 3.1)
Imports:	dplyr (≥ 0.7), generics, knitr, purrr, rlang, 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:	2023-01-18
Author:	Edgar Ruiz [aut, cre], Max Kuhn [aut]
Maintainer:	Edgar Ruiz <edgar 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

Documentation:

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

Downloads:

Package source:	tidypredict_0.5.tar.gz
Windows binaries:	r-devel: tidypredict_0.5.zip, r-release: tidypredict_0.5.zip, r-oldrel: tidypredict_0.5.zip
macOS binaries:	r-release (arm64): tidypredict_0.5.tgz, r-oldrel (arm64): tidypredict_0.5.tgz, r-release (x86_64): tidypredict_0.5.tgz, r-oldrel (x86_64): tidypredict_0.5.tgz
Old sources:	tidypredict archive

Reverse dependencies:

Reverse imports:

dbglm, modeldb

Linking:

Please use the canonical form https://CRAN.R-project.org/package=tidypredict to link to this page.