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: 0.4.3
Depends: R (≥ 3.1)
Imports: dplyr (≥ 0.7), rlang, purrr, knitr, generics, tibble
Suggests: dbplyr, testthat (≥ 2.1.0), randomForest, ranger, earth, rmarkdown, nycflights13, RSQLite, methods, DBI, covr, xgboost, Cubist, mlbench, partykit, yaml, parsnip
Published: 2019-09-03
Author: Edgar Ruiz [aut, cre]
Maintainer: Edgar Ruiz <edgar at rstudio.com>
BugReports: https://github.com/tidymodels/tidypredict/issues
License: GPL-3
URL: https://tidymodels.github.io/tidypredict
NeedsCompilation: no
Materials: README NEWS
In views: ModelDeployment
CRAN checks: tidypredict results

Downloads:

Reference manual: tidypredict.pdf
Vignettes: cubist
glm
lm
mars
non-r
ranger
regression
rf
save
randomForest
tree
xgboost
Package source: tidypredict_0.4.3.tar.gz
Windows binaries: r-devel: tidypredict_0.4.3.zip, r-release: tidypredict_0.4.3.zip, r-oldrel: tidypredict_0.4.3.zip
OS X binaries: r-release: tidypredict_0.4.3.tgz, r-oldrel: tidypredict_0.4.3.tgz
Old sources: tidypredict archive

Reverse dependencies:

Reverse imports: modeldb, tidymodels

Linking:

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