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


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


Package source: tidypredict_0.4.9.tar.gz
Windows binaries: r-devel: tidypredict_0.4.9.zip, r-release: tidypredict_0.4.9.zip, r-oldrel: tidypredict_0.4.9.zip
macOS binaries: r-release (arm64): tidypredict_0.4.9.tgz, r-oldrel (arm64): tidypredict_0.4.9.tgz, r-release (x86_64): tidypredict_0.4.9.tgz, r-oldrel (x86_64): tidypredict_0.4.9.tgz
Old sources: tidypredict archive

Reverse dependencies:

Reverse imports: dbglm, modeldb


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