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. Dozens of model classes are supported; see the "Supported models" article at <https://tidypredict.tidymodels.org/articles/models.html> for the current list.

Version:	1.2.0
Depends:	R (≥ 4.1)
Imports:	cli, dplyr (≥ 0.7), generics, jsonlite, knitr, lifecycle, purrr, rlang (≥ 1.1.1), tibble, tidyr, utils
Suggests:	agua, aorsf, baguette, bonsai, C50, covr, Cubist (≥ 0.5.1), data.table, dbarts, DBI, dbplyr, discrim, earth (≥ 5.1.2), glmnet, h2o, kernlab, klaR, LiblineaR, lightgbm, MASS, mboost, mda, methods, mixOmics, mlbench, modeldata, naivebayes, nnet, nycflights13, parsnip, partykit, plsmod, quantreg, randomForest, ranger (≥ 0.14.1), RhpcBLASctl, rmarkdown, rpart (≥ 4.1.0), RSQLite, rules, sda, sparsediscrim, survival, testthat (≥ 3.2.0), withr, xgboost, xrf, yaml
Published:	2026-09-04
DOI:	10.32614/CRAN.package.tidypredict
Author:	Emil Hvitfeldt [aut, cre], Edgar Ruiz [aut], Max Kuhn [aut], Posit Software, PBC [cph, fnd]
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:

Decision trees, using C5.0 (source, R code) Oblique Random Forest, using aorsf (source, R code) bagger models (source, R code) bart models (source, R code) Cubist models (source, R code) fda models (source, R code) Float precision at split boundaries (source) Generalized Linear Regression (source, R code) glmnet models (source, R code) Gradient boosting, using H2O (source, R code) Support vector machines, using kernlab (source, R code) lda models (source, R code) LightGBM models (source, R code) Linear Regression (source, R code) MARS models via the 'earth' package (source, R code) Model-based boosting, using mboost (source, R code) mixOmics PLS models (source, R code) Supported models (source) multinom models (source, R code) NaiveBayes models (source, R code) nnet models (source, R code) Non-R Models (source, R code) nullmodel models (source, R code) Random Forest - partykit (source, R code) qda 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) sda models (source, R code) sparsediscrim 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) RuleFit, using xrf (source, R code)

Downloads:

Package source:	tidypredict_1.2.0.tar.gz
Windows binaries:	r-devel: tidypredict_1.1.1.zip, r-release: tidypredict_1.1.1.zip, r-oldrel: tidypredict_1.1.1.zip
macOS binaries:	r-release (arm64): tidypredict_1.1.1.tgz, r-oldrel (arm64): tidypredict_1.1.1.tgz, r-release (x86_64): tidypredict_1.1.1.tgz, r-oldrel (x86_64): tidypredict_1.1.1.tgz
Old sources:	tidypredict archive

Reverse dependencies:

Reverse imports:	dbglm, modeldb
Reverse suggests:	orbital

Linking:

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