Tools for Creating Tuning Parameter Values

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

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This package contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package.

The name reflects the idea that tuning predictive models can be like turning a set of dials on a complex machine under duress.

To install:

## for development version:


dials 0.0.2

  • Parameter objects now contain code to finalize their values and a number of helper functions for certain data-specific parameters. A force option can be used to avoid updating the values.
  • Parameter objects are printed differently inside of tibbles.
  • regularization was changed to penalty in a few models to be consistent with this change.
  • batch_size and threshold were added.
  • Added a set of parameters for the textrecipes package issue 16.

dials 0.0.1

  • First CRAN version

Reference manual

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0.0.10 by Hannah Frick, 3 months ago,

Report a bug at

Browse source code at

Authors: Max Kuhn [aut] , Hannah Frick [aut, cre] , RStudio [cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports DiceDesign, dplyr, glue, purrr, rlang, tibble, utils, vctrs, withr

Depends on scales

Suggests covr, kernlab, knitr, rmarkdown, rpart, testthat, xml2

Imported by MachineShop, autostats, baguette, bayesmodels, discrim, finetune, flipr, garchmodels, modeltime, modeltime.resample, rules, stacks, tidymodels, tune.

Suggested by healthyR.ts, modeltime.ensemble, parsnip, tabnet, workflowsets.

See at CRAN