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tidymodels is a “meta-package” formodeling and statistical analysis that shares the underlying designphilosophy, grammar, and data structures of thetidyverse.

It includes a core set of packages that are loaded on startup:

• broom takes the messy output ofbuilt-in functions in R, such aslm,nls, ort.test, and turnsthem into tidy data frames.

• dials has tools to create and managevalues of tuning parameters.

• dplyr contains a grammar for datamanipulation.

• ggplot2 implements a grammar ofgraphics.

• infer is a modern approach tostatistical inference.

• parsnip is a tidy, unifiedinterface to creating models.

• purrr is a functional programmingtoolkit.

• recipes is a general datapreprocessor with a modern interface. It can create model matricesthat incorporate feature engineering, imputation, and other helptools.

• rsample has infrastructure forresampling data so that models can be assessed and empiricallyvalidated.

• tibble has a modern re-imagining ofthe data frame.

• tune contains the functions tooptimize model hyper-parameters.

• workflows has methods to combinepre-processing steps and models into a single object.

• yardstick contains tools forevaluating models (e.g. accuracy, RMSE, etc.).

A list of all tidymodels functions across different CRAN packages can befound athttps://www.tidymodels.org/find/.

You can install the released version of tidymodels fromCRAN with:

install.packages("tidymodels")

Install the development version from GitHub with:

# install.packages("pak")pak::pak("tidymodels/tidymodels")

When loading the package, the versions and conflicts are listed:

library(tidymodels)#> ── Attaching packages ────────────────────────────────────── tidymodels 1.4.0 ──#> ✔ broom        1.0.9     ✔ recipes      1.3.1#> ✔ dials        1.4.2     ✔ rsample      1.3.1#> ✔ dplyr        1.1.4     ✔ tibble       3.3.0#> ✔ ggplot2      3.5.2     ✔ tidyr        1.3.1#> ✔ infer        1.0.9     ✔ tune         2.0.0#> ✔ modeldata    1.5.1     ✔ workflows    1.3.0#> ✔ parsnip      1.3.3     ✔ workflowsets 1.1.1#> ✔ purrr        1.1.0     ✔ yardstick    1.3.2#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──#> ✖ purrr::discard() masks scales::discard()#> ✖ dplyr::filter()  masks stats::filter()#> ✖ dplyr::lag()     masks stats::lag()#> ✖ recipes::step()  masks stats::step()

This project is released with aContributor Code ofConduct.By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, andmachine learning, pleasepost on RStudioCommunity.

• Most issues will likely belong on the GitHub repo of an individualpackage. If you think you have encountered a bug with the tidymodelsmetapackage itself, pleasesubmit anissue.

• Either way, learn how to create and share areprex(a minimal, reproducible example), to clearly communicate about yourcode.

• Check out further details oncontributing guidelines for tidymodelspackages andhow to gethelp.