tidymodels is a “meta-package” for modeling and statistical analysis that shares the underlying design philosophy, grammar, and data structures of the tidyverse.
It includes a core set of packages that are loaded on startup:
broom
takes the messy output of
built-in functions in R, such as lm
, nls
, or t.test
, and turns
them into tidy data frames.
dials
has tools to create and manage
values of tuning parameters.
dplyr
contains a grammar for data
manipulation.
ggplot2
implements a grammar of
graphics.
infer
is a modern approach to
statistical inference.
parsnip
is a tidy, unified
interface to creating models.
purrr
is a functional programming
toolkit.
recipes
is a general data
preprocessor with a modern interface. It can create model matrices
that incorporate feature engineering, imputation, and other help
tools.
rsample
has infrastructure for
resampling data so that models can be assessed and empirically
validated.
tibble
has a modern re-imagining of
the data frame.
tune
contains the functions to
optimize model hyper-parameters.
workflows
has methods to combine
pre-processing steps and models into a single object.
yardstick
contains tools for
evaluating models (e.g. accuracy, RMSE, etc.).
A list of all tidymodels functions across different CRAN packages can be found at https://www.tidymodels.org/find/.
You can install the released version of tidymodels from CRAN 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 1.2.0 ──
#> ✔ broom 1.0.5 ✔ recipes 1.0.10
#> ✔ dials 1.2.1 ✔ rsample 1.2.0
#> ✔ dplyr 1.1.4 ✔ tibble 3.2.1
#> ✔ ggplot2 3.5.0 ✔ tidyr 1.3.1
#> ✔ infer 1.0.6 ✔ tune 1.2.0
#> ✔ modeldata 1.3.0 ✔ workflows 1.1.4
#> ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
#> ✔ purrr 1.0.2 ✔ yardstick 1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
Most issues will likely belong on the GitHub repo of an individual package. If you think you have encountered a bug with the tidymodels metapackage itself, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.
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