recipes: Preprocessing and Feature Engineering Steps for Modeling

A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.

Version:	1.1.0
Depends:	dplyr (≥ 1.1.0), R (≥ 3.6)
Imports:	cli, clock (≥ 0.6.1), generics (≥ 0.1.2), glue, gower, hardhat (≥ 1.4.0), ipred (≥ 0.9-12), lifecycle (≥ 1.0.3), lubridate (≥ 1.8.0), magrittr, Matrix, purrr (≥ 1.0.0), rlang (≥ 1.1.0), stats, tibble, tidyr (≥ 1.0.0), tidyselect (≥ 1.2.0), timeDate, utils, vctrs (≥ 0.5.0), withr
Suggests:	covr, ddalpha, dials (≥ 1.2.0), ggplot2, igraph, kernlab, knitr, modeldata (≥ 0.1.1), parsnip (≥ 1.2.0), RANN, RcppRoll, rmarkdown, rpart, rsample, RSpectra, splines2, testthat (≥ 3.0.0), workflows, xml2
Published:	2024-07-04
DOI:	10.32614/CRAN.package.recipes
Author:	Max Kuhn [aut, cre], Hadley Wickham [aut], Emil Hvitfeldt [aut], Posit Software, PBC [cph, fnd]
Maintainer:	Max Kuhn <max at posit.co>
BugReports:	https://github.com/tidymodels/recipes/issues
License:	MIT + file LICENSE
URL:	https://github.com/tidymodels/recipes, https://recipes.tidymodels.org/
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	recipes results

Documentation:

Reference manual:	recipes.pdf
Vignettes:	Handling categorical predictors Ordering of steps Roles in recipes Selecting variables On skipping steps Introduction to recipes

Downloads:

Package source:	recipes_1.1.0.tar.gz
Windows binaries:	r-devel: recipes_1.1.0.zip, r-release: recipes_1.1.0.zip, r-oldrel: recipes_1.1.0.zip
macOS binaries:	r-release (arm64): recipes_1.1.0.tgz, r-oldrel (arm64): recipes_1.1.0.tgz, r-release (x86_64): recipes_1.1.0.tgz, r-oldrel (x86_64): recipes_1.1.0.tgz
Old sources:	recipes archive

Reverse dependencies:

Reverse depends:	embed, extrasteps, scimo, shinyrecipes, textrecipes, themis
Reverse imports:	actxps, autostats, bestNormalize, caret, correlationfunnel, CSCNet, cvms, D2MCS, easyalluvial, finnts, healthyR.ai, healthyR.ts, HTRX, MachineShop, MLDataR, modeltime.ensemble, modeltime.resample, nestedmodels, sparseR, stabiliser, stacks, text, tidymodels, tidysdm, tidytof, timetk, tune, usemodels, viraldomain, viralmodels, viralx
Reverse suggests:	additive, agua, applicable, baguette, bayesian, brulee, bundle, butcher, DALEXtra, dann, finetune, hardhat, modelgrid, modeltime, offsetreg, orbital, palmerpenguins, probably, rsample, rules, SomaDataIO, swag, tabnet, tfhub, tidyAML, tidybins, tidyclust, tidydann, vetiver, waywiser, workflows, workflowsets

Linking:

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