A B C D E F G L M N P R S T U misc
| add_rowindex | Add a column of row numbers to a data frame | 
| augment.model_fit | Augment data with predictions | 
| autoplot.glmnet | Create a ggplot for a model object | 
| autoplot.model_fit | Create a ggplot for a model object | 
| auto_ml | Automatic Machine Learning | 
| 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 | 
| C5_rules | C5.0 rule-based classification models | 
| case_weights | Using case weights with parsnip | 
| case_weights_allowed | Determine if case weights are used | 
| cforest_train | A wrapper function for conditional inference tree models | 
| 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 | 
| extract_fit_engine.model_fit | Extract elements of a parsnip model object | 
| extract_fit_time.model_fit | Extract elements of a parsnip model object | 
| extract_parameter_dials.model_spec | Extract elements of a parsnip model object | 
| extract_parameter_set_dials.model_spec | Extract elements of a parsnip model object | 
| extract_spec_parsnip.model_fit | Extract elements of a parsnip model object | 
| fit.model_spec | Fit a Model Specification to a Dataset | 
| fit_xy.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_data_frame | Fuzzy conversions | 
| maybe_matrix | Fuzzy conversions | 
| min_cols | Execution-time data dimension checks | 
| min_rows | 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 | 
| multinom_reg | Multinomial regression | 
| multi_predict | Model predictions across many sub-models | 
| multi_predict.default | Model predictions across many sub-models | 
| multi_predict._C5.0 | Model predictions across many sub-models | 
| multi_predict._earth | Model predictions across many sub-models | 
| multi_predict._elnet | Model predictions across many sub-models | 
| multi_predict._glmnetfit | Model predictions across many sub-models | 
| multi_predict._lognet | Model predictions across many sub-models | 
| multi_predict._multnet | Model predictions across many sub-models | 
| multi_predict._torch_mlp | Model predictions across many sub-models | 
| multi_predict._train.kknn | Model predictions across many sub-models | 
| multi_predict._xgb.Booster | Model predictions across many sub-models | 
| 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 | 
| parsnip_update | Updating a model specification | 
| pls | Partial least squares (PLS) | 
| poisson_reg | Poisson regression models | 
| rand_forest | Random forest | 
| repair_call | Repair a model call object | 
| required_pkgs.model_fit | Determine required packages for a model | 
| required_pkgs.model_spec | Determine required packages for a model | 
| req_pkgs | 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 | 
| set_mode | Change elements of a model specification | 
| set_mode.model_spec | Change elements of a model specification | 
| 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 | 
| translate.default | Resolve a Model Specification for a Computational Engine | 
| update.bag_mars | Updating a model specification | 
| update.bag_mlp | Updating a model specification | 
| update.bag_tree | Updating a model specification | 
| update.bart | Updating a model specification | 
| update.boost_tree | Updating a model specification | 
| update.C5_rules | Updating a model specification | 
| update.cubist_rules | Updating a model specification | 
| update.decision_tree | Updating a model specification | 
| update.discrim_flexible | Updating a model specification | 
| update.discrim_linear | Updating a model specification | 
| update.discrim_quad | Updating a model specification | 
| update.discrim_regularized | Updating a model specification | 
| update.gen_additive_mod | Updating a model specification | 
| update.linear_reg | Updating a model specification | 
| update.logistic_reg | Updating a model specification | 
| update.mars | Updating a model specification | 
| update.mlp | Updating a model specification | 
| update.multinom_reg | Updating a model specification | 
| update.naive_Bayes | Updating a model specification | 
| update.nearest_neighbor | Updating a model specification | 
| update.pls | Updating a model specification | 
| update.poisson_reg | Updating a model specification | 
| update.proportional_hazards | Updating a model specification | 
| update.rand_forest | Updating a model specification | 
| update.rule_fit | Updating a model specification | 
| update.survival_reg | Updating a model specification | 
| update.surv_reg | Updating a model specification | 
| update.svm_linear | Updating a model specification | 
| update.svm_poly | Updating a model specification | 
| update.svm_rbf | Updating a model specification | 
| .cols | Data Set Characteristics Available when Fitting Models | 
| .dat | Data Set Characteristics Available when Fitting Models | 
| .extract_surv_status | Extract survival status | 
| .extract_surv_time | Extract survival time | 
| .facts | Data Set Characteristics Available when Fitting Models | 
| .get_prediction_column_names | Obtain names of prediction columns for a fitted model or workflow | 
| .lvls | Data Set Characteristics Available when Fitting Models | 
| .obs | Data Set Characteristics Available when Fitting Models | 
| .preds | Data Set Characteristics Available when Fitting Models | 
| .x | Data Set Characteristics Available when Fitting Models | 
| .y | Data Set Characteristics Available when Fitting Models |