Man pages for parsnip
A Common API to Modeling and Analysis Functions
| add_on_exports | Functions required for parsnip-adjacent packages |
| add_rowindex | Add a column of row numbers to a data frame |
| augment | 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) |
| bart-internal | Developer functions for predictions via BART models |
| boost_tree | Boosted trees |
| C5.0_train | Boosted trees via C5.0 |
| C5_rules | C5.0 rule-based classification models |
| case_weights | Using case weights with parsnip |
| case_weights_allowed | Determine if case weights are used |
| censoring_weights | Calculations for inverse probability of censoring weights... |
| check_empty_ellipse | Check to ensure that ellipses are empty |
| condense_control | Condense control object into strictly smaller control object |
| control_parsnip | Control the fit function |
| contr_one_hot | Contrast function for one-hot encodings |
| convert_helpers | Helper functions to convert between formula and matrix... |
| convert_stan_interval | Convenience function for intervals |
| 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 |
| details_auto_ml_h2o | Automatic machine learning via h2o |
| details_bag_mars_earth | Bagged MARS via earth |
| details_bag_mlp_nnet | Bagged neural networks via nnet |
| details_bag_tree_C5.0 | Bagged trees via C5.0 |
| details_bag_tree_rpart | Bagged trees via rpart |
| details_bart_dbarts | Bayesian additive regression trees via dbarts |
| details_boost_tree_C5.0 | Boosted trees via C5.0 |
| details_boost_tree_h2o | Boosted trees via h2o |
| details_boost_tree_lightgbm | Boosted trees via lightgbm |
| details_boost_tree_mboost | Boosted trees |
| details_boost_tree_spark | Boosted trees via Spark |
| details_boost_tree_xgboost | Boosted trees via xgboost |
| details_C5_rules_C5.0 | C5.0 rule-based classification models |
| details_cubist_rules_Cubist | Cubist rule-based regression models |
| details_decision_tree_C5.0 | Decision trees via C5.0 |
| details_decision_tree_partykit | Decision trees via partykit |
| details_decision_tree_rpart | Decision trees via CART |
| details_decision_tree_spark | Decision trees via Spark |
| details_discrim_flexible_earth | Flexible discriminant analysis via earth |
| details_discrim_linear_MASS | Linear discriminant analysis via MASS |
| details_discrim_linear_mda | Linear discriminant analysis via flexible discriminant... |
| details_discrim_linear_sda | Linear discriminant analysis via James-Stein-type shrinkage... |
| details_discrim_linear_sparsediscrim | Linear discriminant analysis via regularization |
| details_discrim_quad_MASS | Quadratic discriminant analysis via MASS |
| details_discrim_quad_sparsediscrim | Quadratic discriminant analysis via regularization |
| details_discrim_regularized_klaR | Regularized discriminant analysis via klaR |
| details_gen_additive_mod_mgcv | Generalized additive models via mgcv |
| details_linear_reg_brulee | Linear regression via brulee |
| details_linear_reg_gee | Linear regression via generalized estimating equations (GEE) |
| details_linear_reg_glm | Linear regression via glm |
| details_linear_reg_glmer | Linear regression via generalized mixed models |
| details_linear_reg_glmnet | Linear regression via glmnet |
| details_linear_reg_gls | Linear regression via generalized least squares |
| details_linear_reg_h2o | Linear regression via h2o |
| details_linear_reg_keras | Linear regression via keras/tensorflow |
| details_linear_reg_lm | Linear regression via lm |
| details_linear_reg_lme | Linear regression via mixed models |
| details_linear_reg_lmer | Linear regression via mixed models |
| details_linear_reg_spark | Linear regression via spark |
| details_linear_reg_stan | Linear regression via Bayesian Methods |
| details_linear_reg_stan_glmer | Linear regression via hierarchical Bayesian methods |
| details_logistic_reg_brulee | Logistic regression via brulee |
| details_logistic_reg_gee | Logistic regression via generalized estimating equations... |
| details_logistic_reg_glm | Logistic regression via glm |
| details_logistic_reg_glmer | Logistic regression via mixed models |
| details_logistic_reg_glmnet | Logistic regression via glmnet |
| details_logistic_reg_h2o | Logistic regression via h2o |
| details_logistic_reg_keras | Logistic regression via keras |
| details_logistic_reg_LiblineaR | Logistic regression via LiblineaR |
| details_logistic_reg_spark | Logistic regression via spark |
| details_logistic_reg_stan | Logistic regression via stan |
| details_logistic_reg_stan_glmer | Logistic regression via hierarchical Bayesian methods |
| details_mars_earth | Multivariate adaptive regression splines (MARS) via earth |
| details_mlp_brulee | Multilayer perceptron via brulee |
| details_mlp_h2o | Multilayer perceptron via h2o |
| details_mlp_keras | Multilayer perceptron via keras |
| details_mlp_nnet | Multilayer perceptron via nnet |
| details_multinom_reg_brulee | Multinomial regression via brulee |
| details_multinom_reg_glmnet | Multinomial regression via glmnet |
| details_multinom_reg_h2o | Multinomial regression via h2o |
| details_multinom_reg_keras | Multinomial regression via keras |
| details_multinom_reg_nnet | Multinomial regression via nnet |
| details_multinom_reg_spark | Multinomial regression via spark |
| details_naive_Bayes_h2o | Naive Bayes models via naivebayes |
| details_naive_Bayes_klaR | Naive Bayes models via klaR |
| details_naive_Bayes_naivebayes | Naive Bayes models via naivebayes |
| details_nearest_neighbor_kknn | K-nearest neighbors via kknn |
| details_pls_mixOmics | Partial least squares via mixOmics |
| details_poisson_reg_gee | Poisson regression via generalized estimating equations (GEE) |
| details_poisson_reg_glm | Poisson regression via glm |
| details_poisson_reg_glmer | Poisson regression via mixed models |
| details_poisson_reg_glmnet | Poisson regression via glmnet |
| details_poisson_reg_h2o | Poisson regression via h2o |
| details_poisson_reg_hurdle | Poisson regression via pscl |
| details_poisson_reg_stan | Poisson regression via stan |
| details_poisson_reg_stan_glmer | Poisson regression via hierarchical Bayesian methods |
| details_poisson_reg_zeroinfl | Poisson regression via pscl |
| details_proportional_hazards_glmnet | Proportional hazards regression |
| details_proportional_hazards_survival | Proportional hazards regression |
| details_rand_forest_aorsf | Oblique random survival forests via aorsf |
| details_rand_forest_h2o | Random forests via h2o |
| details_rand_forest_partykit | Random forests via partykit |
| details_rand_forest_randomForest | Random forests via randomForest |
| details_rand_forest_ranger | Random forests via ranger |
| details_rand_forest_spark | Random forests via spark |
| details_rule_fit_h2o | RuleFit models via h2o |
| details_rule_fit_xrf | RuleFit models via xrf |
| details_survival_reg_flexsurv | Parametric survival regression |
| details_survival_reg_flexsurvspline | Flexible parametric survival regression |
| details_survival_reg_survival | Parametric survival regression |
| details_svm_linear_kernlab | Linear support vector machines (SVMs) via kernlab |
| details_svm_linear_LiblineaR | Linear support vector machines (SVMs) via LiblineaR |
| details_svm_poly_kernlab | Polynomial support vector machines (SVMs) via kernlab |
| details_svm_rbf_kernlab | Radial basis function support vector machines (SVMs) via... |
| discrim_flexible | Flexible discriminant analysis |
| discrim_linear | Linear discriminant analysis |
| discrim_quad | Quadratic discriminant analysis |
| discrim_regularized | Regularized discriminant analysis |
| doc-tools | Tools for documenting engines |
| dot-extract_surv_status | Extract survival status |
| dot-extract_surv_time | Extract survival time |
| dot-model_param_name_key | Translate names of model tuning parameters |
| eval_args | Evaluate parsnip model arguments |
| extension-check-helpers | Model Specification Checking: |
| extract-parsnip | Extract elements of a parsnip model object |
| fit | Fit a Model Specification to a Dataset |
| fit_control | Control the fit function |
| format-internals | Internal functions that format predictions |
| gen_additive_mod | Generalized additive models (GAMs) |
| get_model_env | Working with the parsnip model environment |
| glance.model_fit | Construct a single row summary "glance" of a model, fit, or... |
| glm_grouped | Fit a grouped binomial outcome from a data set with case... |
| glmnet-details | Technical aspects of the glmnet model |
| glmnet_helpers | Helper functions for checking the penalty of glmnet models |
| glmnet_helpers_prediction | Organize glmnet predictions |
| has_multi_predict | Tools for models that predict on sub-models |
| keras_mlp | Simple interface to MLP models via keras |
| keras_predict_classes | Wrapper for keras class predictions |
| knit_engine_docs | Knit engine-specific documentation |
| linear_reg | Linear regression |
| list_md_problems | Locate and show errors/warnings in engine-specific... |
| logistic_reg | Logistic regression |
| make_call | Make a parsnip call expression |
| make_classes | Prepend a new class |
| mars | Multivariate adaptive regression splines (MARS) |
| max_mtry_formula | Determine largest value of mtry from formula. This function... |
| maybe_matrix | Fuzzy conversions |
| min_cols | Execution-time data dimension checks |
| mlp | Single layer neural network |
| model_db | parsnip model specification database |
| model_fit | Model Fit Object Information |
| model_formula | Formulas with special terms in tidymodels |
| model_printer | Print helper for model objects |
| model_spec | Model Specification Information |
| multinom_reg | Multinomial regression |
| multi_predict | Model predictions across many sub-models |
| naive_Bayes | Naive Bayes models |
| nearest_neighbor | K-nearest neighbors |
| nullmodel | Fit a simple, non-informative model |
| null_model | Null model |
| other_predict | Other predict methods. |
| parsnip_addin | Start an RStudio Addin that can write model specifications |
| parsnip-package | parsnip |
| parsnip_update | Updating a model specification |
| pls | Partial least squares (PLS) |
| poisson_reg | Poisson regression models |
| predict.model_fit | Model predictions |
| prepare_data | Prepare data based on parsnip encoding information |
| proportional_hazards | Proportional hazards regression |
| rand_forest | Random forest |
| reexports | Objects exported from other packages |
| 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 |
| rpart_train | Decision trees via rpart |
| rule_fit | RuleFit models |
| set_args | Change elements of a model specification |
| set_engine | Declare a computational engine and specific arguments |
| set_new_model | Tools to Register Models |
| set_tf_seed | Set seed in R and TensorFlow at the same time |
| show_call | Print the model call |
| show_engines | Display currently available engines for a model |
| stan_conf_int | Wrapper for stan confidence intervals |
| survival_reg | Parametric survival regression |
| surv_reg | Parametric survival regression |
| svm_linear | Linear support vector machines |
| svm_poly | Polynomial support vector machines |
| svm_rbf | Radial basis function support vector machines |
| tidy._elnet | tidy methods for glmnet models |
| tidy._LiblineaR | tidy methods for LiblineaR models |
| tidy.model_fit | Turn a parsnip model object into a tidy tibble |
| tidy.nullmodel | Tidy method for null models |
| translate | Resolve a Model Specification for a Computational Engine |
| type_sum.model_spec | Succinct summary of parsnip object |
| update_model_info_file | Save information about models |
| varying | A placeholder function for argument values |
| varying_args | Determine varying arguments |
| xgb_train | Boosted trees via xgboost |
