Laplace                 Laplace correction parameter
activation              Activation functions between network layers
adjust_deg_free         Parameters to adjust effective degrees of
                        freedom
all_neighbors           Parameter to determine which neighbors to use
bart-param              Parameters for BART models These parameters are
                        used for constructing Bayesian adaptive
                        regression tree (BART) models.
buffer                  Buffer size
cal_method_class        Methods for model calibration
class_weights           Parameters for class weights for imbalanced
                        problems
conditional_min_criterion
                        Parameters for possible engine parameters for
                        partykit models
confidence_factor       Parameters for possible engine parameters for
                        C5.0
cost                    Support vector machine parameters
deg_free                Degrees of freedom (integer)
degree                  Parameters for exponents
dist_power              Minkowski distance parameter
dropout                 Neural network parameters
extrapolation           Parameters for possible engine parameters for
                        Cubist
finalize                Functions to finalize data-specific parameter
                        ranges
freq_cut                Near-zero variance parameters
grid_regular            Create grids of tuning parameters
grid_space_filling      Space-filling parameter grids
harmonic_frequency      Harmonic Frequency
initial_umap            Initialization method for UMAP
learn_rate              Learning rate
max_nodes               Parameters for possible engine parameters for
                        randomForest
max_num_terms           Parameters for possible engine parameters for
                        earth models
max_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
mixture                 Mixture of penalization terms
momentum                Gradient descent momentum parameter
mtry                    Number of randomly sampled predictors
mtry_prop               Proportion of Randomly Selected Predictors
neighbors               Number of neighbors
new-param               Tools for creating new parameter objects
num_breaks              Number of cut-points for binning
num_clusters            Number of Clusters
num_comp                Number of new features
num_hash                Text hashing parameters
num_knots               Number of knots (integer)
num_leaves              Possible engine parameters for lightbgm
num_runs                Number of Computation Runs
num_tokens              Parameter to determine number of tokens in
                        ngram
ordinal_link            Ordinal Regression Link Functions (character)
over_ratio              Parameters for class-imbalance sampling
parameters              Create a parameter set
penalty                 Amount of regularization/penalization
predictor_prop          Proportion of predictors
prior_slab_dispersion   Bayesian PCA parameters
prop_terms              Proportion of top predictors
prune_method            MARS pruning methods
range_limits            Limits for the range of predictions
range_validate          Tools for working with parameter ranges
rbf_sigma               Kernel parameters
regularization_factor   Parameters for possible engine parameters for
                        ranger
regularization_method   Estimation methods for regularized models
scale_pos_weight        Parameters for possible engine parameters for
                        xgboost
scheduler-param         Parameters for neural network learning rate
                        schedulers
select_features         Parameter to enable feature selection
shrinkage_correlation   Parameters for possible engine parameters for
                        sda models
smoothness              Kernel Smoothness
stop_iter               Early stopping parameter
summary_stat            Rolling summary statistic for moving windows
surv_dist               Parametric distributions for censored data
survival_link           Survival Model Link Function
tab-pfn-param           Parameters for TabPFN models
target_weight           Amount of supervision parameter
threshold               General thresholding parameter
token                   Token types
trees                   Parameter functions related to tree- and
                        rule-based models.
trim_amount             Amount of Trimming
unknown                 Placeholder for unknown parameter values
update.parameters       Update a single parameter in a parameter set
validation_set_prop     Proportion of data used for validation
value_validate          Tools for working with parameter values
vocabulary_size         Number of tokens in vocabulary
weight                  Parameter for '"double normalization"' when
                        creating token counts
weight_func             Kernel functions for distance weighting
weight_scheme           Term frequency weighting methods
window_size             Parameter for the moving window size
