.extract_surv_status    Extract survival status
.extract_surv_time      Extract survival time
.get_prediction_column_names
                        Obtain names of prediction columns for a fitted
                        model or workflow
C5_rules                C5.0 rule-based classification models
add_rowindex            Add a column of row numbers to a data frame
augment.model_fit       Augment data with predictions
auto_ml                 Automatic Machine Learning
autoplot.model_fit      Create a ggplot for a model object
bag_mars                Ensembles of MARS models
bag_mlp                 Ensembles of neural networks
bag_tree                Ensembles of decision trees
bart                    Bayesian additive regression trees (BART)
boost_tree              Boosted trees
case_weights            Using case weights with parsnip
case_weights_allowed    Determine if case weights are used
control_parsnip         Control the fit function
ctree_train             A wrapper function for conditional inference
                        tree models
cubist_rules            Cubist rule-based regression models
decision_tree           Decision trees
descriptors             Data Set Characteristics Available when Fitting
                        Models
discrim_flexible        Flexible discriminant analysis
discrim_linear          Linear discriminant analysis
discrim_quad            Quadratic discriminant analysis
discrim_regularized     Regularized discriminant analysis
extract-parsnip         Extract elements of a parsnip model object
fit.model_spec          Fit a Model Specification to a Dataset
gen_additive_mod        Generalized additive models (GAMs)
glance.model_fit        Construct a single row summary "glance" of a
                        model, fit, or other object
glm_grouped             Fit a grouped binomial outcome from a data set
                        with case weights
linear_reg              Linear regression
logistic_reg            Logistic regression
mars                    Multivariate adaptive regression splines (MARS)
max_mtry_formula        Determine largest value of mtry from formula.
                        This function potentially caps the value of
                        'mtry' based on a formula and data set. This is
                        a safe approach for survival and/or
                        multivariate models.
maybe_matrix            Fuzzy conversions
min_cols                Execution-time data dimension checks
mlp                     Single layer neural network
model_fit               Model Fit Objects
model_formula           Formulas with special terms in tidymodels
model_spec              Model Specifications
multi_predict           Model predictions across many sub-models
multinom_reg            Multinomial regression
naive_Bayes             Naive Bayes models
nearest_neighbor        K-nearest neighbors
null_model              Null model
parsnip_addin           Start an RStudio Addin that can write model
                        specifications
pls                     Partial least squares (PLS)
poisson_reg             Poisson regression models
rand_forest             Random forest
repair_call             Repair a model call object
req_pkgs                Determine required packages for a model
required_pkgs.model_spec
                        Determine required packages for a model
rule_fit                RuleFit models
set_args                Change elements of a model specification
set_engine              Declare a computational engine and specific
                        arguments
show_engines            Display currently available engines for a model
sparse_data             Using sparse data with parsnip
svm_linear              Linear support vector machines
svm_poly                Polynomial support vector machines
svm_rbf                 Radial basis function support vector machines
tidy.model_fit          Turn a parsnip model object into a tidy tibble
translate               Resolve a Model Specification for a
                        Computational Engine
update.bag_mars         Updating a model specification
