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dials

Overview

This package contains infrastructure to create and manage values of tuning parameters for the tidymodels packages. If you are looking for how to tune parameters in tidymodels, please look at the tune package and tidymodels.org.

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

Installation

You can install the released version of dials from CRAN with:

install.packages("dials")

You can install the development version from Github with:

# install.packages("pak")
pak::pak("tidymodels/dials")

Contributing

Please note that the dials 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 Posit Community.
If you think you have encountered a bug, 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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Version

Install

install.packages('dials')

Monthly Downloads

30,054

Version

1.4.1

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidymodels/dials

Homepage

https://dials.tidymodels.org

Maintainer

Hannah Frick

Last Published

July 29th, 2025

Functions in dials (1.4.1)

Support vector machine parameters

Class for converting parameter values back and forth to the unit range

Functions to finalize data-specific parameter ranges

Parameters for exponents

Degrees of freedom (integer)

dials: Tools for working with tuning parameters

grid_space_filling

Space-filling parameter grids

grid_max_entropy

Max-entropy and latin hypercube grids

Number of neighbors

Number of Clusters

Near-zero variance parameters

Neural network parameters

Maximum number of retained tokens

Parameters for possible engine parameters for Cubist

Parameter for the effective minimum distance between embedded points

Create grids of tuning parameters

Parameters for possible engine parameters for earth models

Word frequencies for removal

Minkowski distance parameter

Number of randomly sampled predictors

Tools for creating new parameter objects

Parameters for class-imbalance sampling

Information on tuning parameters within an object

Number of unique values for pre-processing

Number of cut-points for binning

harmonic_frequency

Harmonic Frequency

Initialization method for UMAP

Kernel parameters

Objects exported from other packages

regularization_factor

Parameters for possible engine parameters for ranger

Possible engine parameters for lightbgm

parameters_constr

Construct a new parameter set object

regularization_method

Estimation methods for regularized models

General thresholding parameter

Number of Computation Runs

Mixture of penalization terms

Parameter to determine number of tokens in ngram

MARS pruning methods

Parameters for possible engine parameters for randomForest

Proportion of Randomly Selected Predictors

prior_slab_dispersion

Bayesian PCA parameters

select_features

Parameter to enable feature selection

Proportion of predictors

scheduler-param

Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.

Parameter for "double normalization" when creating token counts

Gradient descent momentum parameter

Early stopping parameter

Limits for the range of predictions

Amount of regularization/penalization

Number of new features

Tools for working with parameter ranges

Amount of supervision parameter

Kernel functions for distance weighting

update.parameters

Update a single parameter in a parameter set

Number of knots (integer)

Text hashing parameters

Succinct summary of parameter objects

shrinkage_correlation

Parameters for possible engine parameters for sda models

Kernel Smoothness

Tools for working with parameter values

Survival Model Link Function

Parametric distributions for censored data

Rolling summary statistic for moving windows

validation_set_prop

Proportion of data used for validation

Parameter for the moving window size

Placeholder for unknown parameter values

Term frequency weighting methods

Parameter functions related to tree- and rule-based models.

vocabulary_size

Number of tokens in vocabulary

Amount of Trimming

scale_pos_weight

Parameters for possible engine parameters for xgboost

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

confidence_factor

Parameters for possible engine parameters for C5.0

adjust_deg_free

Parameters to adjust effective degrees of freedom

Parameter to determine which neighbors to use

conditional_min_criterion

Parameters for possible engine parameters for partykit models

Parameters for class weights for imbalanced problems

Laplace correction parameter

Activation functions between network layers

cal_method_class

Methods for model calibration