workflows: Modeling Workflows

Managing both a 'parsnip' model and a preprocessor, such as a model formula or recipe from 'recipes', can often be challenging. The goal of 'workflows' is to streamline this process by bundling the model alongside the preprocessor, all within the same object.

Version: 1.1.4
Depends: R (≥ 3.6)
Imports: cli (≥ 3.3.0), generics (≥ 0.1.2), glue (≥ 1.6.2), hardhat (≥ 1.2.0), lifecycle (≥ 1.0.3), modelenv (≥ 0.1.0), parsnip (≥ 1.2.0), rlang (≥ 1.0.3), tidyselect (≥ 1.2.0), vctrs (≥ 0.4.1)
Suggests: butcher (≥ 0.2.0), covr, dials (≥ 1.0.0), knitr, magrittr, modeldata (≥ 1.0.0), recipes (≥ 1.0.0), rmarkdown, testthat (≥ 3.0.0)
Published: 2024-02-19
DOI: 10.32614/CRAN.package.workflows
Author: Davis Vaughan [aut], Simon Couch ORCID iD [aut, cre], Posit Software, PBC [cph, fnd]
Maintainer: Simon Couch <simon.couch at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: workflows results


Reference manual: workflows.pdf
Vignettes: Workflow Stages


Package source: workflows_1.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): workflows_1.1.4.tgz, r-oldrel (arm64): workflows_1.1.4.tgz, r-release (x86_64): workflows_1.1.4.tgz, r-oldrel (x86_64): workflows_1.1.4.tgz
Old sources: workflows archive

Reverse dependencies:

Reverse imports: agua, autostats, finetune, finnts,, MLDataR, modeltime, modeltime.resample, probably, stacks, text, tidyAML, tidymodels, tidysdm, tune, viraldomain, viralmodels, viralx, workflowsets
Reverse suggests: additive, bayesian, bundle, coefplot, easyalluvial, gtsummary, healthyR.ts, marginaleffects, nestedmodels, offsetreg, orbital, recipes, tabnet, tidyclust, tidydann, timetk, vetiver
Reverse enhances: vip


Please use the canonical form to link to this page.