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: | 1.0.1 |
| Depends: | dplyr, R (≥ 3.4) |
| Imports: | cli, ellipsis, generics (≥ 0.1.2), glue, gower, hardhat (≥ 1.2.0), ipred (≥ 0.9-12), lifecycle, lubridate (≥ 1.8.0), magrittr, Matrix, purrr (≥ 0.2.3), rlang (≥ 1.0.3), stats, tibble, tidyr (≥ 1.0.0), tidyselect (≥ 1.1.2), timeDate, utils, vctrs, withr |
| Suggests: | covr, ddalpha, dials (≥ 1.0.0), ggplot2, igraph, kernlab, knitr, modeldata (≥ 0.1.1), parsnip (≥ 0.1.7), RANN, RcppRoll, rmarkdown, rpart, rsample, RSpectra, testthat (≥ 3.0.0), workflows, xml2 |
| Published: | 2022-07-07 |
| Author: | Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph] |
| Maintainer: | Max Kuhn <max at rstudio.com> |
| 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 |
| Reference manual: | recipes.pdf |
| Vignettes: |
Handling categorical predictors Ordering of steps Roles in recipes Selecting variables On skipping steps Introduction to recipes |
| Package source: | recipes_1.0.1.tar.gz |
| Windows binaries: | r-devel: recipes_1.0.1.zip, r-release: recipes_1.0.1.zip, r-oldrel: recipes_1.0.1.zip |
| macOS binaries: | r-release (arm64): recipes_1.0.1.tgz, r-oldrel (arm64): recipes_1.0.1.tgz, r-release (x86_64): recipes_1.0.1.tgz, r-oldrel (x86_64): recipes_1.0.1.tgz |
| Old sources: | recipes archive |
| Reverse depends: | embed, hydrorecipes, shinyrecipes, textrecipes, themis |
| Reverse imports: | autostats, bestNormalize, card, caret, correlationfunnel, cvms, D2MCS, easyalluvial, finnts, healthyR.ai, healthyR.ts, MachineShop, MLDataR, modeltime.ensemble, modeltime.resample, stabiliser, stacks, text, tidymodels, timetk, tune, usemodels |
| Reverse suggests: | additive, applicable, baguette, bayesian, brulee, butcher, DALEXtra, finetune, hardhat, modelgrid, modeltime, palmerpenguins, rsample, rules, sknifedatar, swag, tabnet, tfhub, tidybins, vetiver, workboots, workflows, workflowsets |
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