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(), 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

Documentation:

Reference manual:

tidypredict.html , tidypredict.pdf

Vignettes:

CatBoost models (source, R code) Cubist models (source, R code) Float precision at split boundaries (source) Generalized Linear Regression (source, R code) glmnet models (source, R code) LightGBM models (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) Decision trees, using rpart (source, R code) Save and re-load models (source, R code) Database write-back (source, R code) How tidypredict generates tree formulas (source, R code) Create a tree spec - version 2 (source, R code) XGBoost models (source, R code)

Downloads:

Package source:	tidypredict_1.1.0.tar.gz
Windows binaries:	r-devel: tidypredict_1.1.0.zip, r-release: tidypredict_1.1.0.zip, r-oldrel: tidypredict_1.1.0.zip
macOS binaries:	r-release (arm64): tidypredict_1.1.0.tgz, r-oldrel (arm64): tidypredict_1.1.0.tgz, r-release (x86_64): tidypredict_1.1.0.tgz, r-oldrel (x86_64): tidypredict_1.1.0.tgz
Old sources:	tidypredict archive

Reverse dependencies:

Reverse imports:	dbglm, modeldb
Reverse suggests:	orbital

Linking:

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