Package index • dials

Parameter sets
`parameters()`	Information on tuning parameters within an object
`update(<parameters>)`	Update a single parameter in a parameter set
`lower_limit()` `upper_limit()`	Limits for the range of predictions
`range_validate()` `range_get()` `range_set()`	Tools for working with parameter ranges
`value_validate()` `value_seq()` `value_sample()` `value_transform()` `value_inverse()` `value_set()`	Tools for working with parameter values
Grid creation
`grid_regular()` `grid_random()`	Create grids of tuning parameters
`grid_space_filling()`	Space-filling parameter grids
Parameter objects for preprocessing
`all_neighbors()`	Parameter to determine which neighbors to use
`freq_cut()` `unique_cut()`	Near-zero variance parameters
`harmonic_frequency()`	Harmonic Frequency
`initial_umap()` `values_initial_umap`	Initialization method for UMAP
`max_times()` `min_times()`	Word frequencies for removal
`max_tokens()`	Maximum number of retained tokens
`min_dist()`	Parameter for the effective minimum distance between embedded points
`min_unique()`	Number of unique values for pre-processing
`num_breaks()`	Number of cut-points for binning
`num_hash()` `signed_hash()`	Text hashing parameters
`num_runs()`	Number of Computation Runs
`num_tokens()`	Parameter to determine number of tokens in ngram
`over_ratio()` `under_ratio()`	Parameters for class-imbalance sampling
`prior_slab_dispersion()` `prior_mixture_threshold()`	Bayesian PCA parameters
`prop_terms()`	Proportion of top predictors
`token()` `values_token`	Token types
`trim_amount()`	Amount of Trimming
`validation_set_prop()`	Proportion of data used for validation
`vocabulary_size()`	Number of tokens in vocabulary
`weight()`	Parameter for `"double normalization"` when creating token counts
`weight_scheme()` `values_weight_scheme`	Term frequency weighting methods
`window_size()`	Parameter for the moving window size
Parameter objects for modeling
`activation()` `activation_2()` `values_activation`	Activation functions between network layers
`adjust_deg_free()`	Parameters to adjust effective degrees of freedom
`class_weights()`	Parameters for class weights for imbalanced problems
`cost()` `svm_margin()`	Support vector machine parameters
`deg_free()`	Degrees of freedom (integer)
`degree()` `degree_int()` `spline_degree()` `prod_degree()`	Parameters for exponents
`dist_power()`	Minkowski distance parameter
`dropout()` `epochs()` `hidden_units()` `hidden_units_2()` `batch_size()`	Neural network parameters
`Laplace()`	Laplace correction parameter
`learn_rate()`	Learning rate
`mixture()`	Mixture of penalization terms
`momentum()`	Gradient descent momentum parameter
`mtry()` `mtry_long()`	Number of randomly sampled predictors
`mtry_prop()`	Proportion of Randomly Selected Predictors
`neighbors()`	Number of neighbors
`num_clusters()`	Number of Clusters
`num_comp()` `num_terms()`	Number of new features
`num_knots()`	Number of knots (integer)
`penalty()`	Amount of regularization/penalization
`predictor_prop()`	Proportion of predictors
`prune_method()` `values_prune_method`	MARS pruning methods
`rate_initial()` `rate_largest()` `rate_reduction()` `rate_steps()` `rate_step_size()` `rate_decay()` `rate_schedule()` `values_scheduler`	Parameters for neural network learning rate schedulers These parameters are used for constructing neural network models.
`rbf_sigma()` `scale_factor()` `kernel_offset()`	Kernel parameters
`regularization_method()` `values_regularization_method`	Estimation methods for regularized models
`select_features()`	Parameter to enable feature selection
`smoothness()`	Kernel Smoothness
`stop_iter()`	Early stopping parameter
`summary_stat()` `values_summary_stat`	Rolling summary statistic for moving windows
`surv_dist()` `values_surv_dist`	Parametric distributions for censored data
`survival_link()` `values_survival_link`	Survival Model Link Function
`target_weight()`	Amount of supervision parameter
`threshold()`	General thresholding parameter
`trees()` `min_n()` `sample_size()` `sample_prop()` `loss_reduction()` `tree_depth()` `prune()` `cost_complexity()`	Parameter functions related to tree- and rule-based models.
`weight_func()` `values_weight_func`	Kernel functions for distance weighting
Parameter objects for specific model engines
`prior_terminal_node_coef()` `prior_terminal_node_expo()` `prior_outcome_range()`	Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.
`conditional_min_criterion()` `values_test_type` `conditional_test_type()` `values_test_statistic` `conditional_test_statistic()`	Parameters for possible engine parameters for partykit models
`confidence_factor()` `no_global_pruning()` `predictor_winnowing()` `fuzzy_thresholding()` `rule_bands()`	Parameters for possible engine parameters for C5.0
`extrapolation()` `unbiased_rules()` `max_rules()`	Parameters for possible engine parameters for Cubist
`max_nodes()`	Parameters for possible engine parameters for randomForest
`max_num_terms()`	Parameters for possible engine parameters for earth models
`num_leaves()`	Possible engine parameters for lightbgm
`regularization_factor()` `regularize_depth()` `significance_threshold()` `lower_quantile()` `splitting_rule()` `ranger_class_rules` `ranger_reg_rules` `ranger_split_rules` `num_random_splits()`	Parameters for possible engine parameters for ranger
`scale_pos_weight()` `penalty_L2()` `penalty_L1()`	Parameters for possible engine parameters for xgboost
`shrinkage_correlation()` `shrinkage_variance()` `shrinkage_frequencies()` `diagonal_covariance()`	Parameters for possible engine parameters for sda models
Parameter objects for post-processing
`buffer()`	Buffer size
`lower_limit()` `upper_limit()`	Limits for the range of predictions
`cal_method_class()` `cal_method_reg()` `values_cal_cls` `values_cal_reg`	Methods for model calibration
Finalizing parameters
`finalize()` `get_p()` `get_log_p()` `get_n_frac()` `get_n_frac_range()` `get_n()` `get_rbf_range()`	Functions to finalize data-specific parameter ranges
Developer tools
`encode_unit()`	Class for converting parameter values back and forth to the unit range
`new_quant_param()` `new_qual_param()`	Tools for creating new parameter objects
`parameters_constr()`	Construct a new parameter set object
`unknown()` `is_unknown()` `has_unknowns()`	Placeholder for unknown parameter values

Reference

Parameter sets

Grid creation

Parameter objects for preprocessing

Parameter objects for modeling

Parameter objects for specific model engines

Parameter objects for post-processing

Finalizing parameters

Developer tools