CRAN/E | tidypredict

tidypredict

Run Predictions Inside the Database

Installation

About

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.

tidypredict.tidymodels.org
github.com/tidymodels/tidypredict
Bug report File report

Key Metrics

Version 0.5
R ≥ 3.1
Published 2023-01-18 465 days ago
Needs compilation? no
License MIT
License File
CRAN checks tidypredict results

Downloads

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Maintainer

Maintainer

Edgar Ruiz

edgar@posit.co

Authors

Edgar Ruiz

aut / cre

Max Kuhn

aut

Material

README
NEWS
Reference manual
Package source

In Views

ModelDeployment

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

tidypredict archive

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

Reverse Imports

dbglm
modeldb