Search all of tidymodels

Here are all the functions available across all of the tidymodels packages. Click on a title to open its reference documentation.

"Reshuffle" an rset to re-generate a new rset with the same parameters

function: reshuffle_rset package: rsample

A CART classification tree tuned via racing

function: cell_race package: shinymodels

A convenience function for confidence intervals with linear-ish parametric models

function: reg_intervals package: rsample

A scoring function

function: score package: applicable

A scoring function

function: score.default package: applicable

A wrapper function for conditional inference tree models

function: ctree_train package: parsnip

A wrapper function for conditional inference tree models

function: cforest_train package: parsnip

Accuracy

function: accuracy package: yardstick

Accuracy

function: accuracy.data.frame package: yardstick

Accuracy

function: accuracy_vec package: yardstick

Acquisition function for scoring parameter combinations

function: prob_improve package: tune

Acquisition function for scoring parameter combinations

function: exp_improve package: tune

Acquisition function for scoring parameter combinations

function: conf_bound package: tune

Activation functions between network layers

function: activation package: dials

Activation functions between network layers

function: activation_2 package: dials

Activation functions between network layers

function: values_activation package: dials

Activation functions for neural networks in brulee

function: brulee_activations package: brulee

Adaptive Synthetic Algorithm

function: adasyn package: themis

Add Assessment Indices

function: populate package: rsample

Add WoE in a data frame

function: add_woe package: embed

Add a New Operation to the Current Recipe

function: add_step package: recipes

Add a New Operation to the Current Recipe

function: add_check package: recipes

Add a class_pred column

function: append_class_pred package: probably

Add a column of row numbers to a data frame

function: add_rowindex package: parsnip

Add a first column to a data.frame

function: first_col package: corrr

Add a model to a workflow

function: add_model package: workflows

Add a model to a workflow

function: remove_model package: workflows

Add a model to a workflow

function: update_model package: workflows

Add a recipe to a workflow

function: add_recipe package: workflows

Add a recipe to a workflow

function: remove_recipe package: workflows

Add a recipe to a workflow

function: update_recipe package: workflows

Add a tailor to a workflow

function: add_tailor package: workflows

Add a tailor to a workflow

function: remove_tailor package: workflows

Add a tailor to a workflow

function: update_tailor package: workflows

Add an intercept column to data

function: add_intercept_column package: hardhat

Add and edit options saved in a workflow set

function: option_add package: workflowsets

Add and edit options saved in a workflow set

function: option_remove package: workflowsets

Add and edit options saved in a workflow set

function: option_add_parameters package: workflowsets

Add annotations and comments for workflows

function: comment_add package: workflowsets

Add annotations and comments for workflows

function: comment_get package: workflowsets

Add annotations and comments for workflows

function: comment_reset package: workflowsets

Add annotations and comments for workflows

function: comment_print package: workflowsets

Add case weights to a workflow

function: add_case_weights package: workflows

Add case weights to a workflow

function: remove_case_weights package: workflows

Add case weights to a workflow

function: update_case_weights package: workflows

Add fitted values, residuals, and other common outputs to an augment call

function: augment_columns package: broom

Add formula terms to a workflow

function: add_formula package: workflows

Add formula terms to a workflow

function: remove_formula package: workflows

Add formula terms to a workflow

function: update_formula package: workflows

Add information about confidence interval

function: shade_confidence_interval package: infer

Add information about confidence interval

function: shade_ci package: infer

Add intercept (or constant) column

function: step_intercept package: recipes

Add model definitions to a data stack

function: add_candidates package: stacks

Add new variables using dplyr

function: step_mutate package: recipes

Add resample weights to an rset object

function: add_resample_weights package: tune

Add sin and cos terms for harmonic analysis

function: step_harmonic package: recipes

Add variables to a workflow

function: add_variables package: workflows

Add variables to a workflow

function: remove_variables package: workflows

Add variables to a workflow

function: update_variables package: workflows

Add variables to a workflow

function: workflow_variables package: workflows

Adds the prediction columns to a piped command set.

function: tidypredict_to_column package: tidypredict

Alcohol, Cigarette, and Marijuana Use for High School Seniors

function: seniors package: poissonreg

Alzheimer's disease data

function: ad_data package: modeldata

Ames Housing Data

function: ames package: modeldata

Ames exampled score results

function: ames_scores_results package: filtro

Amount of Trimming

function: trim_amount package: dials

Amount of regularization/penalization

function: penalty package: dials

Amount of supervision parameter

function: target_weight package: dials

Annual Stack Overflow Developer Survey Data

function: stackoverflow package: modeldata

Applicability domain methods using binary similarity analysis

function: apd_similarity package: applicable

Applicability domain methods using binary similarity analysis

function: apd_similarity.default package: applicable

Applicability domain methods using binary similarity analysis

function: apd_similarity.data.frame package: applicable

Applicability domain methods using binary similarity analysis

function: apd_similarity.matrix package: applicable

Applicability domain methods using binary similarity analysis

function: apd_similarity.formula package: applicable

Applicability domain methods using binary similarity analysis

function: apd_similarity.recipe package: applicable

Applies a calibration to a set of existing predictions

function: cal_apply package: probably

Applies a calibration to a set of existing predictions

function: cal_apply.data.frame package: probably

Applies a calibration to a set of existing predictions

function: cal_apply.tune_results package: probably

Applies a calibration to a set of existing predictions

function: cal_apply.cal_object package: probably

Apply (smoothed) rectified linear transformation

function: step_relu package: recipes

Apply Adaptive Synthetic Algorithm

function: step_adasyn package: themis

Apply Adaptive Synthetic Algorithm

function: tidy.step_adasyn package: themis

Apply ROSE Algorithm

function: step_rose package: themis

Apply ROSE Algorithm

function: tidy.step_rose package: themis

Apply SMOTE Algorithm

function: step_smote package: themis

Apply SMOTE Algorithm

function: tidy.step_smote package: themis

Apply SMOTENC algorithm

function: step_smotenc package: themis

Apply SMOTENC algorithm

function: tidy.step_smotenc package: themis

Apply a function to all pairs of columns in a data frame

function: colpair_map package: corrr

Apply a trained preprocessing recipe

function: bake package: recipes

Apply a trained preprocessing recipe

function: bake.recipe package: recipes

Apply borderline-SMOTE Algorithm

function: step_bsmote package: themis

Apply borderline-SMOTE Algorithm

function: tidy.step_bsmote package: themis

Area under the ROC curve of each class against the rest, using the a priori class distribution

function: roc_aunp package: yardstick

Area under the ROC curve of each class against the rest, using the a priori class distribution

function: roc_aunp.data.frame package: yardstick

Area under the ROC curve of each class against the rest, using the a priori class distribution

function: roc_aunp_vec package: yardstick

Area under the ROC curve of each class against the rest, using the uniform class distribution

function: roc_aunu package: yardstick

Area under the ROC curve of each class against the rest, using the uniform class distribution

function: roc_aunu.data.frame package: yardstick

Area under the ROC curve of each class against the rest, using the uniform class distribution

function: roc_aunu_vec package: yardstick

Area under the precision recall curve

function: average_precision package: yardstick

Area under the precision recall curve

function: average_precision.data.frame package: yardstick

Area under the precision recall curve

function: average_precision_vec package: yardstick

Area under the precision recall curve

function: pr_auc package: yardstick

Area under the precision recall curve

function: pr_auc.data.frame package: yardstick

Area under the precision recall curve

function: pr_auc_vec package: yardstick

Area under the receiver operator curve

function: roc_auc package: yardstick

Area under the receiver operator curve

function: roc_auc.data.frame package: yardstick

Area under the receiver operator curve

function: roc_auc_vec package: yardstick

Arrange score

function: arrange_score package: filtro

Assign missing categories to "unknown"

function: step_unknown package: recipes

Augment a data set with resampling identifiers

function: add_resample_id package: rsample

Augment a model stack

function: augment.model_stack package: stacks

Augment data with holdout predictions

function: augment.tune_results package: tune

Augment data with holdout predictions

function: augment.resample_results package: tune

Augment data with holdout predictions

function: augment.last_fit package: tune

Augment data with information from a(n) Mclust object

function: augment.Mclust package: broom

Augment data with information from a(n) betamfx object

function: augment.betamfx package: broom

Augment data with information from a(n) betareg object

function: augment.betareg package: broom

Augment data with information from a(n) clm object

function: augment.clm package: broom

Augment data with information from a(n) coxph object

function: augment.coxph package: broom

Augment data with information from a(n) decomposed.ts object

function: augment.decomposed.ts package: broom

Augment data with information from a(n) decomposed.ts object

function: decompose_tidiers package: broom

Augment data with information from a(n) drc object

function: augment.drc package: broom

Augment data with information from a(n) factanal object

function: augment.factanal package: broom

Augment data with information from a(n) felm object

function: augment.felm package: broom

Augment data with information from a(n) fixest object

function: augment.fixest package: broom

Augment data with information from a(n) gam object

function: augment.gam package: broom

Augment data with information from a(n) glm object

function: augment.glm package: broom

Augment data with information from a(n) glmRob object

function: augment.glmRob package: broom

Augment data with information from a(n) glmrob object

function: augment.glmrob package: broom

Augment data with information from a(n) htest object

function: augment.htest package: broom

Augment data with information from a(n) ivreg object

function: augment.ivreg package: broom

Augment data with information from a(n) kmeans object

function: augment.kmeans package: broom

Augment data with information from a(n) lm object

function: augment.lm package: broom

Augment data with information from a(n) lmRob object

function: augment.lmRob package: broom

Augment data with information from a(n) lmrob object

function: augment.lmrob package: broom

Augment data with information from a(n) mfx object

function: augment.mfx package: broom

Augment data with information from a(n) mfx object

function: augment.logitmfx package: broom

Augment data with information from a(n) mfx object

function: augment.negbinmfx package: broom

Augment data with information from a(n) mfx object

function: augment.poissonmfx package: broom

Augment data with information from a(n) mfx object

function: augment.probitmfx package: broom

Augment data with information from a(n) mjoint object

function: augment.mjoint package: broom

Augment data with information from a(n) mlogit object

function: augment.mlogit package: broom

Augment data with information from a(n) nls object

function: augment.nls package: broom

Augment data with information from a(n) pam object

function: augment.pam package: broom

Augment data with information from a(n) plm object

function: augment.plm package: broom

Augment data with information from a(n) poLCA object

function: augment.poLCA package: broom

Augment data with information from a(n) polr object

function: augment.polr package: broom

Augment data with information from a(n) prcomp object

function: augment.prcomp package: broom

Augment data with information from a(n) rlm object

function: augment.rlm package: broom

Augment data with information from a(n) rma object

function: augment.rma package: broom

Augment data with information from a(n) rq object

function: augment.rq package: broom

Augment data with information from a(n) rqs object

function: augment.rqs package: broom

Augment data with information from a(n) spatialreg object

function: augment.sarlm package: broom

Augment data with information from a(n) speedlm object

function: augment.speedlm package: broom

Augment data with information from a(n) stl object

function: augment.stl package: broom

Augment data with information from a(n) survreg object

function: augment.survreg package: broom

Augment data with predictions

function: augment.model_fit package: parsnip

Augment data with predictions

function: augment.cluster_fit package: tidyclust

Augment data with predictions

function: augment.workflow package: workflows

Augment using orbital objects

function: augment.orbital_class package: orbital

Automatic Machine Learning

function: auto_ml package: parsnip

Axe a call.

function: axe_call package: butcher

Axe an environment.

function: axe_env package: butcher

Axe controls.

function: axe_ctrl package: butcher

Axe data within rsample objects.

function: axe-rsample-data package: butcher

Axe data within rsample objects.

function: axe_rsample_data package: butcher

Axe data within rsample objects.

function: axe_rsample_data.default package: butcher

Axe data within rsample objects.

function: axe_rsample_data.rsplit package: butcher

Axe data within rsample objects.

function: axe_rsample_data.three_way_split package: butcher

Axe data within rsample objects.

function: axe_rsample_data.rset package: butcher

Axe data within rsample objects.

function: axe_rsample_data.tune_results package: butcher

Axe data within rsample objects.

function: axe_rsample_data.workflow_set package: butcher

Axe data.

function: axe_data package: butcher

Axe fitted values.

function: axe_fitted package: butcher

Axe indicators within rsample objects.

function: axe-rsample-indicators package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.default package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.rsplit package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.three_way_split package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.rset package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.tune_results package: butcher

Axe indicators within rsample objects.

function: axe_rsample_indicators.workflow_set package: butcher

Axing a C5.0.

function: axe-C5.0 package: butcher

Axing a C5.0.

function: axe_call.C5.0 package: butcher

Axing a C5.0.

function: axe_ctrl.C5.0 package: butcher

Axing a C5.0.

function: axe_fitted.C5.0 package: butcher

Axing a KMeansCluster.

function: axe-KMeansCluster package: butcher

Axing a KMeansCluster.

function: axe_call.KMeansCluster package: butcher

Axing a KMeansCluster.

function: axe_fitted.KMeansCluster package: butcher

Axing a MASS discriminant analysis object.

function: axe-mass package: butcher

Axing a MASS discriminant analysis object.

function: axe-lda package: butcher

Axing a MASS discriminant analysis object.

function: axe-qda package: butcher

Axing a MASS discriminant analysis object.

function: axe_env.lda package: butcher

Axing a MASS discriminant analysis object.

function: axe_env.qda package: butcher

Axing a MASS discriminant analysis object.

function: axe_env.polr package: butcher

Axing a NaiveBayes.

function: axe-NaiveBayes package: butcher

Axing a NaiveBayes.

function: axe_call.NaiveBayes package: butcher

Axing a NaiveBayes.

function: axe_data.NaiveBayes package: butcher

Axing a bagged tree.

function: axe-ipred package: butcher

Axing a bagged tree.

function: axe-regbagg package: butcher

Axing a bagged tree.

function: axe-classbagg package: butcher

Axing a bagged tree.

function: axe-survbagg package: butcher

Axing a bagged tree.

function: axe_call.regbagg package: butcher

Axing a bagged tree.

function: axe_call.classbagg package: butcher

Axing a bagged tree.

function: axe_call.survbagg package: butcher

Axing a bagged tree.

function: axe_ctrl.regbagg package: butcher

Axing a bagged tree.

function: axe_ctrl.classbagg package: butcher

Axing a bagged tree.

function: axe_ctrl.survbagg package: butcher

Axing a bagged tree.

function: axe_data.regbagg package: butcher

Axing a bagged tree.

function: axe_data.classbagg package: butcher

Axing a bagged tree.

function: axe_data.survbagg package: butcher

Axing a bagged tree.

function: axe_env.regbagg package: butcher

Axing a bagged tree.

function: axe_env.classbagg package: butcher

Axing a bagged tree.

function: axe_env.survbagg package: butcher

Axing a bart model.

function: axe-bart package: butcher

Axing a bart model.

function: axe_call.bart package: butcher

Axing a bart model.

function: axe_fitted.bart package: butcher

Axing a cluster_fit.

function: axe-cluster_fit package: tidyclust

Axing a cluster_fit.

function: axe_call.cluster_fit package: tidyclust

Axing a cluster_fit.

function: axe_ctrl.cluster_fit package: tidyclust

Axing a cluster_fit.

function: axe_data.cluster_fit package: tidyclust

Axing a cluster_fit.

function: axe_env.cluster_fit package: tidyclust

Axing a cluster_fit.

function: axe_fitted.cluster_fit package: tidyclust

Axing a coxph.

function: axe-coxph package: butcher

Axing a coxph.

function: axe_env.coxph package: butcher

Axing a coxph.

function: axe_data.coxph package: butcher

Axing a gam.

function: axe-gam package: butcher

Axing a gam.

function: axe_call.gam package: butcher

Axing a gam.

function: axe_ctrl.gam package: butcher

Axing a gam.

function: axe_data.gam package: butcher

Axing a gam.

function: axe_env.gam package: butcher

Axing a gam.

function: axe_fitted.gam package: butcher

Axing a gausspr.

function: axe-gausspr package: butcher

Axing a gausspr.

function: axe_call.gausspr package: butcher

Axing a gausspr.

function: axe_data.gausspr package: butcher

Axing a gausspr.

function: axe_env.gausspr package: butcher

Axing a gausspr.

function: axe_fitted.gausspr package: butcher

Axing a glm.

function: axe-glm package: butcher

Axing a glm.

function: axe_call.glm package: butcher

Axing a glm.

function: axe_data.glm package: butcher

Axing a glm.

function: axe_env.glm package: butcher

Axing a glm.

function: axe_fitted.glm package: butcher

Axing a glmnet.

function: axe-glmnet package: butcher

Axing a glmnet.

function: axe_call.glmnet package: butcher

Axing a kproto.

function: axe-kproto package: butcher

Axing a kproto.

function: axe_data.kproto package: butcher

Axing a kproto.

function: axe_fitted.kproto package: butcher

Axing a ksvm object.

function: axe-ksvm package: butcher

Axing a ksvm object.

function: axe_call.ksvm package: butcher

Axing a ksvm object.

function: axe_data.ksvm package: butcher

Axing a ksvm object.

function: axe_fitted.ksvm package: butcher

Axing a mda.

function: axe-mda package: butcher

Axing a mda.

function: axe_call.mda package: butcher

Axing a mda.

function: axe_call.fda package: butcher

Axing a mda.

function: axe_env.mda package: butcher

Axing a mda.

function: axe_env.fda package: butcher

Axing a mda.

function: axe_fitted.mda package: butcher

Axing a mda.

function: axe_fitted.fda package: butcher

Axing a model_stack.

function: axe_model_stack package: stacks

Axing a model_stack.

function: axe_call.model_stack package: stacks

Axing a model_stack.

function: axe_ctrl.model_stack package: stacks

Axing a model_stack.

function: axe_data.model_stack package: stacks

Axing a model_stack.

function: axe_env.model_stack package: stacks

Axing a model_stack.

function: axe_fitted.model_stack package: stacks

Axing a nnet.

function: axe-nnet package: butcher

Axing a nnet.

function: axe_call.nnet package: butcher

Axing a nnet.

function: axe_env.nnet package: butcher

Axing a nnet.

function: axe_fitted.nnet package: butcher

Axing a recipe object.

function: axe-recipe package: butcher

Axing a recipe object.

function: axe_env.recipe package: butcher

Axing a recipe object.

function: axe_env.step package: butcher

Axing a recipe object.

function: axe_env.step_arrange package: butcher

Axing a recipe object.

function: axe_env.step_filter package: butcher

Axing a recipe object.

function: axe_env.step_mutate package: butcher

Axing a recipe object.

function: axe_env.step_slice package: butcher

Axing a recipe object.

function: axe_env.step_impute_bag package: butcher

Axing a recipe object.

function: axe_env.step_bagimpute package: butcher

Axing a recipe object.

function: axe_env.step_impute_knn package: butcher

Axing a recipe object.

function: axe_env.step_knnimpute package: butcher

Axing a recipe object.

function: axe_env.step_geodist package: butcher

Axing a recipe object.

function: axe_env.step_interact package: butcher

Axing a recipe object.

function: axe_env.step_ratio package: butcher

Axing a recipe object.

function: axe_env.quosure package: butcher

Axing a recipe object.

function: axe_fitted.recipe package: butcher

Axing a rpart.

function: axe-rpart package: butcher

Axing a rpart.

function: axe_call.rpart package: butcher

Axing a rpart.

function: axe_ctrl.rpart package: butcher

Axing a rpart.

function: axe_data.rpart package: butcher

Axing a rpart.

function: axe_env.rpart package: butcher

Axing a sclass object.

function: axe-sclass package: butcher

Axing a sclass object.

function: axe_call.sclass package: butcher

Axing a sclass object.

function: axe_env.sclass package: butcher

Axing a spark object.

function: axe-spark package: butcher

Axing a spark object.

function: axe_call.ml_model package: butcher

Axing a spark object.

function: axe_ctrl.ml_model package: butcher

Axing a spark object.

function: axe_data.ml_model package: butcher

Axing a spark object.

function: axe_fitted.ml_model package: butcher

Axing a tabnet_fit.

function: axe-tabnet_fit package: butcher

Axing a tabnet_fit.

function: axe_fitted._tabnet_fit package: butcher

Axing a train object.

function: axe-train package: butcher

Axing a train object.

function: axe_call.train package: butcher

Axing a train object.

function: axe_ctrl.train package: butcher

Axing a train object.

function: axe_data.train package: butcher

Axing a train object.

function: axe_env.train package: butcher

Axing a train object.

function: axe_fitted.train package: butcher

Axing a train.recipe object.

function: axe-train.recipe package: butcher

Axing a train.recipe object.

function: axe_call.train.recipe package: butcher

Axing a train.recipe object.

function: axe_ctrl.train.recipe package: butcher

Axing a train.recipe object.

function: axe_data.train.recipe package: butcher

Axing a train.recipe object.

function: axe_env.train.recipe package: butcher

Axing a train.recipe object.

function: axe_fitted.train.recipe package: butcher

Axing a xgb.Booster.

function: axe-xgb.Booster package: butcher

Axing a xgb.Booster.

function: axe_call.xgb.Booster package: butcher

Axing a xgb.Booster.

function: axe_env.xgb.Booster package: butcher

Axing a xrf.

function: axe-xrf package: butcher

Axing a xrf.

function: axe_call.xrf package: butcher

Axing a xrf.

function: axe_env.xrf package: butcher

Axing an earth object.

function: axe-earth package: butcher

Axing an earth object.

function: axe_call.earth package: butcher

Axing an earth object.

function: axe_data.earth package: butcher

Axing an earth object.

function: axe_fitted.earth package: butcher

Axing an elnet.

function: axe-elnet package: butcher

Axing an elnet.

function: axe_call.elnet package: butcher

Axing an flexsurvreg.

function: axe-flexsurvreg package: butcher

Axing an flexsurvreg.

function: axe_call.flexsurvreg package: butcher

Axing an flexsurvreg.

function: axe_env.flexsurvreg package: butcher

Axing an kknn.

function: axe-kknn package: butcher

Axing an kknn.

function: axe_env.kknn package: butcher

Axing an kknn.

function: axe_fitted.kknn package: butcher

Axing an lm.

function: axe-lm package: butcher

Axing an lm.

function: axe_call.lm package: butcher

Axing an lm.

function: axe_env.lm package: butcher

Axing an lm.

function: axe_fitted.lm package: butcher

Axing an model_fit.

function: axe-model_fit package: butcher

Axing an model_fit.

function: axe_call.model_fit package: butcher

Axing an model_fit.

function: axe_ctrl.model_fit package: butcher

Axing an model_fit.

function: axe_data.model_fit package: butcher

Axing an model_fit.

function: axe_env.model_fit package: butcher

Axing an model_fit.

function: axe_fitted.model_fit package: butcher

Axing an multnet.

function: axe-multnet package: butcher

Axing an multnet.

function: axe_call.multnet package: butcher

Axing an randomForest.

function: axe-randomForest package: butcher

Axing an randomForest.

function: axe_call.randomForest package: butcher

Axing an randomForest.

function: axe_ctrl.randomForest package: butcher

Axing an randomForest.

function: axe_env.randomForest package: butcher

Axing an ranger.

function: axe-ranger package: butcher

Axing an ranger.

function: axe_call.ranger package: butcher

Axing an ranger.

function: axe_fitted.ranger package: butcher

Axing an rda.

function: axe-rda package: butcher

Axing an rda.

function: axe-klaR package: butcher

Axing an rda.

function: axe_call.rda package: butcher

Axing an rda.

function: axe_env.rda package: butcher

Axing an survreg.

function: axe-survreg package: butcher

Axing an survreg.

function: axe_call.survreg package: butcher

Axing an survreg.

function: axe_data.survreg package: butcher

Axing an survreg.

function: axe_env.survreg package: butcher

Axing an survreg.penal

function: axe-survreg.penal package: butcher

Axing an survreg.penal

function: axe_call.survreg.penal package: butcher

Axing an survreg.penal

function: axe_data.survreg.penal package: butcher

Axing an survreg.penal

function: axe_env.survreg.penal package: butcher

Axing for terms inputs.

function: axe-terms package: butcher

Axing for terms inputs.

function: axe_env.terms package: butcher

Axing formulas.

function: axe-formula package: butcher

Axing formulas.

function: axe_env.formula package: butcher

Axing functions.

function: axe-function package: butcher

Axing functions.

function: axe_env.function package: butcher

Axing mixOmics models

function: axe-pls package: butcher

Axing mixOmics models

function: axe-mixo_pls package: butcher

Axing mixOmics models

function: axe_call.mixo_pls package: butcher

Axing mixOmics models

function: axe_call.mixo_spls package: butcher

Axing mixOmics models

function: axe_data.mixo_pls package: butcher

Axing mixOmics models

function: axe_data.mixo_spls package: butcher

Axing mixOmics models

function: axe_fitted.mixo_pls package: butcher

Axing mixOmics models

function: axe_fitted.mixo_spls package: butcher

B-spline basis functions

function: step_bs package: recipes

BPE Tokenization of Character Variables

function: step_tokenize_bpe package: textrecipes

BPE Tokenization of Character Variables

function: tidy.step_tokenize_bpe package: textrecipes

Bagging functions

function: bagger package: baguette

Bagging functions

function: bagger.default package: baguette

Bagging functions

function: bagger.data.frame package: baguette

Bagging functions

function: bagger.matrix package: baguette

Bagging functions

function: bagger.formula package: baguette

Bagging functions

function: bagger.recipe package: baguette

Balanced accuracy

function: bal_accuracy package: yardstick

Balanced accuracy

function: bal_accuracy.data.frame package: yardstick

Balanced accuracy

function: bal_accuracy_vec package: yardstick

Bandwidth

function: bandwidth package: tidyclust

Basis splines

function: step_spline_b package: recipes

Bayesian Analysis of Resampling Statistics

function: perf_mod package: tidyposterior

Bayesian Analysis of Resampling Statistics

function: perf_mod.rset package: tidyposterior

Bayesian Analysis of Resampling Statistics

function: perf_mod.resamples package: tidyposterior

Bayesian Analysis of Resampling Statistics

function: perf_mod.data.frame package: tidyposterior

Bayesian Analysis of Resampling Statistics

function: perf_mod.tune_results package: tidyposterior

Bayesian Analysis of Resampling Statistics

function: perf_mod.workflow_set package: tidyposterior

Bayesian PCA parameters

function: prior_slab_dispersion package: dials

Bayesian PCA parameters

function: prior_mixture_threshold package: dials

Bayesian additive regression trees (BART)

function: bart package: parsnip

Bayesian optimization of model parameters.

function: tune_bayes package: tune

Bayesian optimization of model parameters.

function: tune_bayes.model_spec package: tune

Bayesian optimization of model parameters.

function: tune_bayes.workflow package: tune

Binary QSAR Data

function: binary package: applicable

Binary QSAR Data

function: qsar_binary package: applicable

Binary QSAR Data

function: binary_tr package: applicable

Binary QSAR Data

function: binary_unk package: applicable

Bind score class object, including their associated metadata and scores

function: bind_scores package: filtro

Biomass data

function: biomass package: modeldata

Boosted regression trees predictions

function: boosting_predictions package: probably

Boosted regression trees predictions

function: boosting_predictions_oob package: probably

Boosted regression trees predictions

function: boosting_predictions_test package: probably

Boosted trees

function: boost_tree package: parsnip

Bootstrap Sampling

function: bootstraps package: rsample

Bootstrap confidence intervals

function: int_pctl package: rsample

Bootstrap confidence intervals

function: int_pctl.default package: rsample

Bootstrap confidence intervals

function: int_pctl.bootstraps package: rsample

Bootstrap confidence intervals

function: int_t package: rsample

Bootstrap confidence intervals

function: int_t.default package: rsample

Bootstrap confidence intervals

function: int_t.bootstraps package: rsample

Bootstrap confidence intervals

function: int_bca package: rsample

Bootstrap confidence intervals

function: int_bca.default package: rsample

Bootstrap confidence intervals

function: int_bca.bootstraps package: rsample

Bootstrap confidence intervals for performance metrics

function: int_pctl.tune_results package: tune

Boston tree canopy and heat index data.

function: boston_canopy package: spatialsample

Box-Cox transformation for non-negative data

function: step_BoxCox package: recipes

Brier score for classification models

function: brier_class package: yardstick

Brier score for classification models

function: brier_class.data.frame package: yardstick

Brier score for classification models

function: brier_class_vec package: yardstick

Buffer size

function: buffer package: dials

Butcher an object.

function: butcher package: butcher

Butcher methods for a workflow

function: workflow-butcher package: workflows

Butcher methods for a workflow

function: axe_call.workflow package: workflows

Butcher methods for a workflow

function: axe_ctrl.workflow package: workflows

Butcher methods for a workflow

function: axe_data.workflow package: workflows

Butcher methods for a workflow

function: axe_env.workflow package: workflows

Butcher methods for a workflow

function: axe_fitted.workflow package: workflows

C5.0 rule-based classification models

function: C5_rules package: parsnip

Calculate LDA Dimension Estimates of Tokens

function: step_lda package: textrecipes

Calculate LDA Dimension Estimates of Tokens

function: tidy.step_lda package: textrecipes

Calculate Set of Text Features

function: step_textfeature package: textrecipes

Calculate Set of Text Features

function: tidy.step_textfeature package: textrecipes

Calculate and format metrics from tuning functions

function: compute_metrics package: tune

Calculate and format metrics from tuning functions

function: compute_metrics.default package: tune

Calculate and format metrics from tuning functions

function: compute_metrics.tune_results package: tune

Calculate observed statistics

function: observe package: infer

Calculate resample weights from resample sizes

function: calculate_resample_weights package: tune

Calculate summary statistics

function: calculate package: infer

Calculate the reportable rate

function: reportable_rate package: probably

Calculates Sum of Squared Error in each cluster

function: sse_within package: tidyclust

Cat Adoption

function: cat_adoption package: modeldata

Cell body segmentation

function: cells package: modeldata

Center and scale numeric data

function: step_normalize package: recipes

Centering numeric data

function: step_center package: recipes

Change arguments of a cluster specification

function: set_args.cluster_spec package: tidyclust

Change elements of a model specification

function: set_args package: parsnip

Change elements of a model specification

function: set_mode package: parsnip

Change elements of a model specification

function: set_mode.model_spec package: parsnip

Change engine of a cluster specification

function: set_engine.cluster_spec package: tidyclust

Change mode of a cluster specification

function: set_mode.cluster_spec package: tidyclust

Change the learning rate over time

function: schedule_decay_time package: brulee

Change the learning rate over time

function: schedule_decay_expo package: brulee

Change the learning rate over time

function: schedule_step package: brulee

Change the learning rate over time

function: schedule_cyclic package: brulee

Change the learning rate over time

function: set_learn_rate package: brulee

Check for missing values

function: check_missing package: recipes

Check for new values

function: check_new_values package: recipes

Check if all columns are present

function: check_cols package: recipes

Check range consistency

function: check_range package: recipes

Check to see if a recipe is trained/prepared

function: fully_trained package: recipes

Check variable class

function: check_class package: recipes

Checks that steps have all S3 methods

function: recipes_extension_check package: recipes

Checks that the formula can be parsed

function: acceptable_formula package: tidypredict

Chemical manufacturing process data set

function: chem_proc_yield package: modeldata

Chicago Features Example Data

function: chi_features_set package: workflowsets

Chicago Features Example Data

function: chi_features_res package: workflowsets

Chicago ridership data

function: Chicago package: modeldata

Chicago ridership data

function: stations package: modeldata

Chicago taxi data set

function: taxi package: modeldata

Class probability predictions

function: hpc_cv package: modeldata

Clean Categorical Levels

function: step_clean_levels package: textrecipes

Clean Categorical Levels

function: tidy.step_clean_levels package: textrecipes

Clean Variable Names

function: step_clean_names package: textrecipes

Clean Variable Names

function: tidy.step_clean_names package: textrecipes

Clinical data used to predict ischemic stroke

function: ischemic_stroke package: modeldata

Cluster Cross-Validation

function: clustering_cv package: rsample

Coerce lists and matrices to correlation data frames

function: as_cordf package: corrr

Coerce to a class_pred object

function: as_class_pred package: probably

Collapse infrequent categorical levels

function: step_other package: recipes

Collect candidate parameters and stacking coefficients

function: collect_parameters package: stacks

Collect candidate parameters and stacking coefficients

function: collect_parameters.default package: stacks

Collect candidate parameters and stacking coefficients

function: collect_parameters.data_stack package: stacks

Collect candidate parameters and stacking coefficients

function: collect_parameters.model_stack package: stacks

Combine Multiple Token Variables Into One

function: step_tokenmerge package: textrecipes

Combine Multiple Token Variables Into One

function: tidy.step_tokenmerge package: textrecipes

Combine metric functions

function: cluster_metric_set package: tidyclust

Combine metric functions

function: metric_set package: yardstick

Compatibility with dplyr

function: rsample-dplyr package: rsample

Compound solubility data

function: solubility package: embed

Compressive strength of concrete mixtures

function: concrete package: modeldata

Compute average confusion matrix across resamples

function: conf_mat_resampled package: tune

Compute confidence interval

function: get_confidence_interval package: infer

Compute confidence interval

function: get_ci package: infer

Compute p-value

function: get_p_value package: infer

Compute p-value

function: get_p_value.default package: infer

Compute p-value

function: get_pvalue package: infer

Compute p-value

function: get_p_value.infer_dist package: infer

Compute permutation-based predictor importance

function: importance_perm package: important

Compute shrunken centroid distances for classification models

function: step_classdist_shrunken package: recipes

Compute the ratio of the WSS to the total SSE

function: sse_ratio package: tidyclust

Compute the ratio of the WSS to the total SSE

function: sse_ratio.cluster_spec package: tidyclust

Compute the ratio of the WSS to the total SSE

function: sse_ratio.cluster_fit package: tidyclust

Compute the ratio of the WSS to the total SSE

function: sse_ratio.workflow package: tidyclust

Compute the ratio of the WSS to the total SSE

function: sse_ratio_vec package: tidyclust

Compute the sum of within-cluster SSE

function: sse_within_total package: tidyclust

Compute the sum of within-cluster SSE

function: sse_within_total.cluster_spec package: tidyclust

Compute the sum of within-cluster SSE

function: sse_within_total.cluster_fit package: tidyclust

Compute the sum of within-cluster SSE

function: sse_within_total.workflow package: tidyclust

Compute the sum of within-cluster SSE

function: sse_within_total_vec package: tidyclust

Compute the total sum of squares

function: sse_total package: tidyclust

Compute the total sum of squares

function: sse_total.cluster_spec package: tidyclust

Compute the total sum of squares

function: sse_total.cluster_fit package: tidyclust

Compute the total sum of squares

function: sse_total.workflow package: tidyclust

Compute the total sum of squares

function: sse_total_vec package: tidyclust

Compute weighted interval score

function: weighted_interval_score package: yardstick

Compute weighted interval score

function: weighted_interval_score.data.frame package: yardstick

Compute weighted interval score

function: weighted_interval_score_vec package: yardstick

Computes distance from observations to centroids

function: get_centroid_dists package: tidyclust

Concordance correlation coefficient

function: ccc package: yardstick

Concordance correlation coefficient

function: ccc.data.frame package: yardstick

Concordance correlation coefficient

function: ccc_vec package: yardstick

Concordance index for right-censored data

function: concordance_survival package: yardstick

Concordance index for right-censored data

function: concordance_survival.data.frame package: yardstick

Concordance index for right-censored data

function: concordance_survival_vec package: yardstick

Conditionally focus correlation data frame

function: focus_if package: corrr

Conflicts between the tidymodels and other packages

function: tidymodels_conflicts package: tidymodels

Confusion Matrix for Categorical Data

function: conf_mat package: yardstick

Confusion Matrix for Categorical Data

function: conf_mat.table package: yardstick

Confusion Matrix for Categorical Data

function: conf_mat.default package: yardstick

Confusion Matrix for Categorical Data

function: conf_mat.data.frame package: yardstick

Confusion Matrix for Categorical Data

function: tidy.conf_mat package: yardstick

Construct a design matrix

function: model_matrix package: hardhat

Construct a frequency weights vector

function: new_frequency_weights package: hardhat

Construct a model frame

function: model_frame package: hardhat

Construct a new clustering metric function

function: new_cluster_metric package: tidyclust

Construct a new metric function

function: new-metric package: yardstick

Construct a new metric function

function: new_class_metric package: yardstick

Construct a new metric function

function: new_prob_metric package: yardstick

Construct a new metric function

function: new_ordered_prob_metric package: yardstick

Construct a new metric function

function: new_numeric_metric package: yardstick

Construct a new metric function

function: new_dynamic_survival_metric package: yardstick

Construct a new metric function

function: new_integrated_survival_metric package: yardstick

Construct a new metric function

function: new_static_survival_metric package: yardstick

Construct a new metric function

function: new_linear_pred_survival_metric package: yardstick

Construct a new metric function

function: new_quantile_metric package: yardstick

Construct a single node of a tree

function: generate_tree_node package: tidypredict

Construct a single row summary "glance" of a model, fit, or other object

function: glance.model_fit package: parsnip

Construct a single row summary "glance" of a model, fit, or other object

function: glance.cluster_fit package: tidyclust

Construct an importance weights vector

function: new_importance_weights package: hardhat

Constructor for a base model

function: new_model package: hardhat

Constructors for split objects

function: make_splits package: rsample

Constructors for split objects

function: make_splits.default package: rsample

Constructors for split objects

function: make_splits.list package: rsample

Constructors for split objects

function: make_splits.data.frame package: rsample

Control aspects of the Bayesian search process

function: control_bayes package: tune

Control aspects of the grid search racing process

function: control_race package: finetune

Control aspects of the last fit process

function: control_last_fit package: tune

Control aspects of the simulated annealing search process

function: control_sim_anneal package: finetune

Control model tuning via h2o::h2o.grid()

function: agua_backend_options package: agua

Control object for a workflow

function: control_workflow package: workflows

Control the fit function

function: control_parsnip package: parsnip

Control wrappers

function: control_stack package: stacks

Control wrappers

function: control_stack_grid package: stacks

Control wrappers

function: control_stack_resamples package: stacks

Control wrappers

function: control_stack_bayes package: stacks

Controlling the bagging process

function: control_bag package: baguette

Controlling the numeric details for conformal inference

function: control_conformal_full package: probably

Convert Resampling Objects to Other Formats

function: rsample2caret package: rsample

Convert Resampling Objects to Other Formats

function: caret2rsample package: rsample

Convert a correlation data frame to matrix format

function: as_matrix package: corrr

Convert an rsplit object to a data frame

function: as.data.frame.rsplit package: rsample

Convert an rsplit object to a data frame

function: analysis package: rsample

Convert an rsplit object to a data frame

function: analysis.default package: rsample

Convert an rsplit object to a data frame

function: analysis.rsplit package: rsample

Convert an rsplit object to a data frame

function: assessment package: rsample

Convert an rsplit object to a data frame

function: assessment.default package: rsample

Convert an rsplit object to a data frame

function: assessment.rsplit package: rsample

Convert data to torch format

function: matrix_to_dataset package: brulee

Convert existing objects to a workflow set

function: as_workflow_set package: workflowsets

Convert factors to strings

function: step_factor2string package: recipes

Convert numbers to factors

function: step_num2factor package: recipes

Convert orbital objects to quosures

function: orbital_inline package: orbital

Convert ordered factors to unordered factors

function: step_unorder package: recipes

Convert ordinal factors to numeric scores

function: step_ordinalscore package: recipes

Convert strings to factors

function: step_string2factor package: recipes

Convert to SQL code

function: orbital_sql package: orbital

Convert to data.table code

function: orbital_dt package: orbital

Convert values to predefined integers

function: step_integer package: recipes

Converts an R model object into a parsed model

function: parse_model package: tidypredict

Convex splines

function: step_spline_convex package: recipes

Correlation Data Frame

function: correlate package: corrr

Cost parameter for minority class

function: class_cost package: baguette

Costs function for poor classification

function: classification_cost package: yardstick

Costs function for poor classification

function: classification_cost.data.frame package: yardstick

Costs function for poor classification

function: classification_cost_vec package: yardstick

Create Token Object

function: tokenlist package: textrecipes

Create a Validation Split for Tuning

function: validation_set package: rsample

Create a Validation Split for Tuning

function: analysis.val_split package: rsample

Create a Validation Split for Tuning

function: assessment.val_split package: rsample

Create a Validation Split for Tuning

function: training.val_split package: rsample

Create a Validation Split for Tuning

function: validation.val_split package: rsample

Create a Validation Split for Tuning

function: testing.val_split package: rsample

Create a class prediction object

function: class_pred package: probably

Create a class_pred vector from class probabilities

function: make_class_pred package: probably

Create a class_pred vector from class probabilities

function: make_two_class_pred package: probably

Create a correlation matrix from a cor_df object

function: autoplot.cor_df package: corrr

Create a factors from A dummy variable

function: step_bin2factor package: recipes

Create a formula from a prepared recipe

function: formula.recipe package: recipes

Create a ggplot for a model object

function: autoplot.model_fit package: parsnip

Create a ggplot for a model object

function: autoplot.glmnet package: parsnip

Create a ggplot for spatial resamples.

function: autoplot.spatial_rset package: spatialsample

Create a ggplot for spatial resamples.

function: autoplot.spatial_block_cv package: spatialsample

Create a lagged predictor

function: step_lag package: recipes

Create a modeling package

function: modeling-usethis package: hardhat

Create a modeling package

function: create_modeling_package package: hardhat

Create a modeling package

function: use_modeling_deps package: hardhat

Create a modeling package

function: use_modeling_files package: hardhat

Create a new default blueprint

function: new_default_formula_blueprint package: hardhat

Create a new default blueprint

function: new_default_recipe_blueprint package: hardhat

Create a new default blueprint

function: new-default-blueprint package: hardhat

Create a new default blueprint

function: new_default_xy_blueprint package: hardhat

Create a new preprocessing blueprint

function: new_formula_blueprint package: hardhat

Create a new preprocessing blueprint

function: new_recipe_blueprint package: hardhat

Create a new preprocessing blueprint

function: new_xy_blueprint package: hardhat

Create a new preprocessing blueprint

function: new-blueprint package: hardhat

Create a new preprocessing blueprint

function: new_blueprint package: hardhat

Create a parameter set

function: parameters package: dials

Create a parameter set

function: parameters.default package: dials

Create a parameter set

function: parameters.param package: dials

Create a parameter set

function: parameters.list package: dials

Create a profiling version of a data set

function: step_profile package: recipes

Create a recipe for preprocessing data

function: recipe package: recipes

Create a recipe for preprocessing data

function: recipe.default package: recipes

Create a recipe for preprocessing data

function: recipe.data.frame package: recipes

Create a recipe for preprocessing data

function: recipe.formula package: recipes

Create a recipe for preprocessing data

function: recipe.matrix package: recipes

Create a vector containing sets of quantiles

function: quantile_pred package: hardhat

Create a vector containing sets of quantiles

function: is_quantile_pred package: hardhat

Create a vector containing sets of quantiles

function: extract_quantile_levels package: hardhat

Create a vector containing sets of quantiles

function: as_tibble.quantile_pred package: hardhat

Create a vector containing sets of quantiles

function: as.matrix.quantile_pred package: hardhat

Create a workflow

function: workflow package: workflows

Create an Initial Train/Validation/Test Split

function: initial_validation_split package: rsample

Create an Initial Train/Validation/Test Split

function: initial_validation_time_split package: rsample

Create an Initial Train/Validation/Test Split

function: group_initial_validation_split package: rsample

Create an Initial Train/Validation/Test Split

function: training.initial_validation_split package: rsample

Create an Initial Train/Validation/Test Split

function: testing.initial_validation_split package: rsample

Create an Initial Train/Validation/Test Split

function: validation package: rsample

Create an Initial Train/Validation/Test Split

function: validation.default package: rsample

Create an Initial Train/Validation/Test Split

function: validation.initial_validation_split package: rsample

Create counts of patterns using regular expressions

function: step_count package: recipes

Create formulas without each predictor

function: leave_var_out_formulas package: workflowsets

Create grids of tuning parameters

function: grid_regular package: dials

Create grids of tuning parameters

function: grid_regular.default package: dials

Create grids of tuning parameters

function: grid_regular.parameters package: dials

Create grids of tuning parameters

function: grid_regular.list package: dials

Create grids of tuning parameters

function: grid_regular.param package: dials

Create grids of tuning parameters

function: grid_random package: dials

Create grids of tuning parameters

function: grid_random.default package: dials

Create grids of tuning parameters

function: grid_random.parameters package: dials

Create grids of tuning parameters

function: grid_random.list package: dials

Create grids of tuning parameters

function: grid_random.param package: dials

Create groupwise metrics

function: new_groupwise_metric package: yardstick

Create interaction variables

function: step_interact package: recipes

Create missing data column indicators

function: step_indicate_na package: recipes

Create or Modify Stratification Variables

function: make_strata package: rsample

Create traditional dummy variables

function: step_dummy package: recipes

Creates a data frame from a stretched correlation table

function: retract package: corrr

Creates dummy variables

function: add_dummy_variables package: modeldb

Credit data

function: credit_data package: modeldata

Crosstable with woe between a binary outcome and a predictor variable.

function: woe_table package: embed

Cubist rule-based regression models

function: cubist_rules package: parsnip

Customer churn data

function: mlc_churn package: modeldata

Cut Height

function: cut_height package: tidyclust

Cut a numeric variable into a factor

function: step_cut package: recipes

Daily Hotel Rate Data

function: hotel_rates package: modeldata

Data Set Characteristics Available when Fitting Models

function: descriptors package: parsnip

Data conversion tools

function: as_h2o package: agua

Data conversion tools

function: as_tibble.H2OFrame package: agua

Data depths

function: step_depth package: recipes

Date feature generator

function: step_date package: recipes

Decision trees

function: decision_tree package: parsnip

Declare a computational engine and specific arguments

function: set_engine package: parsnip

Declare a null hypothesis

function: hypothesize package: infer

Declare a null hypothesis

function: hypothesise package: infer

Default XY blueprint

function: default_xy_blueprint package: hardhat

Default XY blueprint

function: mold.data.frame package: hardhat

Default XY blueprint

function: mold.matrix package: hardhat

Default formula blueprint

function: default_formula_blueprint package: hardhat

Default formula blueprint

function: mold.formula package: hardhat

Default recipe blueprint

function: default_recipe_blueprint package: hardhat

Default recipe blueprint

function: mold.recipe package: hardhat

Define a theoretical distribution

function: assume package: infer

Degrees of freedom (integer)

function: deg_free package: dials

Delete the response from a terms object

function: delete_response package: hardhat

Demographic parity

function: demographic_parity package: yardstick

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

function: db_clust package: tidyclust

Detect a regular expression

function: step_regex package: recipes

Detect if a particular step or check is used in a recipe

function: detect_step package: recipes

Detection prevalence

function: detection_prevalence package: yardstick

Detection prevalence

function: detection_prevalence.data.frame package: yardstick

Detection prevalence

function: detection_prevalence_vec package: yardstick

Determine if a workflow has been trained

function: is_trained_workflow package: workflows

Determine if case weights are used

function: case_weights_allowed package: parsnip

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.

function: max_mtry_formula package: parsnip

Determine object is has class unsupervised_fit

function: is_unsupervised_fit package: modelenv

Determine object is has class unsupervised_spec

function: is_unsupervised_spec package: modelenv

Determine required packages for a model

function: req_pkgs package: parsnip

Determine required packages for a model

function: required_pkgs.model_spec package: parsnip

Determine required packages for a model

function: required_pkgs.model_fit package: parsnip

Determine stacking coefficients from a data stack

function: blend_predictions package: stacks

Determine the Assessment Samples

function: complement package: rsample

Determine the Assessment Samples

function: complement.rsplit package: rsample

Determine the Assessment Samples

function: complement.rof_split package: rsample

Determine the Assessment Samples

function: complement.sliding_window_split package: rsample

Determine the Assessment Samples

function: complement.sliding_index_split package: rsample

Determine the Assessment Samples

function: complement.sliding_period_split package: rsample

Determine the Assessment Samples

function: complement.apparent_split package: rsample

Determine the minimum set of model fits

function: min_grid.cluster_spec package: tidyclust

Developer function for checking inputs in new metrics

function: check_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_numeric_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_class_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_prob_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_ordered_prob_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_dynamic_survival_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_static_survival_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_linear_pred_survival_metric package: yardstick

Developer function for checking inputs in new metrics

function: check_quantile_metric package: yardstick

Developer function for handling missing values in new metrics

function: yardstick_remove_missing package: yardstick

Developer function for handling missing values in new metrics

function: yardstick_any_missing package: yardstick

Developer function for summarizing new metrics

function: metric-summarizers package: yardstick

Developer function for summarizing new metrics

function: numeric_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: class_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: prob_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: ordered_prob_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: curve_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: dynamic_survival_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: static_survival_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: curve_survival_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: linear_pred_survival_metric_summarizer package: yardstick

Developer function for summarizing new metrics

function: quantile_metric_summarizer package: yardstick

Developer functions for creating recipes steps

function: developer_functions package: recipes

Developer helpers

function: developer-helpers package: yardstick

Developer helpers

function: dots_to_estimate package: yardstick

Developer helpers

function: get_weights package: yardstick

Developer helpers

function: finalize_estimator package: yardstick

Developer helpers

function: finalize_estimator_internal package: yardstick

Developer helpers

function: validate_estimator package: yardstick

Disable -log10 transformation of p-values

function: dont_log_pvalues package: filtro

Discretize Numeric Variables

function: discretize package: recipes

Discretize Numeric Variables

function: discretize.default package: recipes

Discretize Numeric Variables

function: discretize.numeric package: recipes

Discretize Numeric Variables

function: predict.discretize package: recipes

Discretize Numeric Variables

function: step_discretize package: recipes

Discretize numeric variables with CART

function: step_discretize_cart package: embed

Discretize numeric variables with CART

function: tidy.step_discretize_cart package: embed

Discretize numeric variables with XgBoost

function: step_discretize_xgb package: embed

Discretize numeric variables with XgBoost

function: tidy.step_discretize_xgb package: embed

Display currently available engines for a model

function: show_engines package: parsnip

Display distinct errors from tune objects

function: show_notes package: tune

Distance between two locations

function: step_geodist package: recipes

Distance to ROC corner

function: roc_dist package: yardstick

Distance to ROC corner

function: roc_dist.data.frame package: yardstick

Distance to ROC corner

function: roc_dist_vec package: yardstick

Distances to class centroids

function: step_classdist package: recipes

Do not calibrate model predictions.

function: cal_estimate_none package: probably

Do not calibrate model predictions.

function: cal_estimate_none.data.frame package: probably

Do not calibrate model predictions.

function: cal_estimate_none.tune_results package: probably

Do not calibrate model predictions.

function: cal_estimate_none.grouped_df package: probably

Down-Sample a Data Set Based on a Factor Variable

function: step_downsample package: themis

Down-Sample a Data Set Based on a Factor Variable

function: tidy.step_downsample package: themis

Early stopping parameter

function: stop_iter package: dials

Efficient grid search via racing with ANOVA models

function: tune_race_anova package: finetune

Efficient grid search via racing with ANOVA models

function: tune_race_anova.model_spec package: finetune

Efficient grid search via racing with ANOVA models

function: tune_race_anova.workflow package: finetune

Efficient grid search via racing with win/loss statistics

function: tune_race_win_loss package: finetune

Efficient grid search via racing with win/loss statistics

function: tune_race_win_loss.model_spec package: finetune

Efficient grid search via racing with win/loss statistics

function: tune_race_win_loss.workflow package: finetune

Encode a factor as a one-hot indicator matrix

function: fct_encode_one_hot package: hardhat

Encoding Factors into Multiple Columns

function: step_embed package: embed

Encoding Factors into Multiple Columns

function: tidy.step_embed package: embed

Encoding Factors into Multiple Columns

function: embed_control package: embed

Ensembles of MARS models

function: bag_mars package: parsnip

Ensembles of decision trees

function: bag_tree package: parsnip

Ensembles of neural networks

function: bag_mlp package: parsnip

Ensure an object is a data frame, with rownames moved into a column

function: fix_data_frame package: broom

Ensure no duplicate terms appear in formula

function: validate_no_formula_duplication package: hardhat

Ensure no duplicate terms appear in formula

function: check_no_formula_duplication package: hardhat

Ensure outcomes are all numeric

function: validate_outcomes_are_numeric package: hardhat

Ensure outcomes are all numeric

function: check_outcomes_are_numeric package: hardhat

Ensure predictors are all numeric

function: validate_predictors_are_numeric package: hardhat

Ensure predictors are all numeric

function: check_predictors_are_numeric package: hardhat

Ensure that data contains required column names

function: validate_column_names package: hardhat

Ensure that data contains required column names

function: check_column_names package: hardhat

Ensure that predictions have the correct number of rows

function: validate_prediction_size package: hardhat

Ensure that predictions have the correct number of rows

function: check_prediction_size package: hardhat

Ensure that the outcome has binary factors

function: validate_outcomes_are_binary package: hardhat

Ensure that the outcome has binary factors

function: check_outcomes_are_binary package: hardhat

Ensure that the outcome has only factor columns

function: validate_outcomes_are_factors package: hardhat

Ensure that the outcome has only factor columns

function: check_outcomes_are_factors package: hardhat

Ensure that the outcome is univariate

function: validate_outcomes_are_univariate package: hardhat

Ensure that the outcome is univariate

function: check_outcomes_are_univariate package: hardhat

Equal opportunity

function: equal_opportunity package: yardstick

Equalized odds

function: equalized_odds package: yardstick

Error handling for incompatible modes

function: stop_incompatible_mode package: modelenv

Error handling for unknown mode

function: check_spec_mode_engine_val package: modelenv

Estimate a preprocessing recipe

function: prep package: recipes

Estimate a preprocessing recipe

function: prep.recipe package: recipes

Estimate orbital expression character count

function: estimate_orbital_size package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.xgb.Booster package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.lgb.Booster package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.ranger package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.randomForest package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.rpart package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.constparty package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.catboost.Model package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.glm package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.lm package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.glmnet package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.earth package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.recipe package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.workflow package: orbital

Estimate orbital expression character count

function: estimate_orbital_size.tailor package: orbital

Estimate the Difference Between Models

function: contrast_models package: tidyposterior

Estimation methods for regularized models

function: regularization_method package: dials

Estimation methods for regularized models

function: values_regularization_method package: dials

Evaluate a selection with tidyselect semantics for arguments

function: recipes_argument_select package: recipes

Evaluate a selection with tidyselect semantics specific to recipes

function: recipes_eval_select package: recipes

Example Analysis of Ames Housing Data

function: example_ames_knn package: tune

Example Analysis of Ames Housing Data

function: ames_wflow package: tune

Example Analysis of Ames Housing Data

function: ames_grid_search package: tune

Example Analysis of Ames Housing Data

function: ames_iter_search package: tune

Example Data Sets

function: precise_example package: tidyposterior

Example Data Sets

function: noisy_example package: tidyposterior

Example Data Sets

function: concrete_example package: tidyposterior

Example Data Sets

function: ts_example package: tidyposterior

Example Data Sets

function: ex_object package: tidyposterior

Example Data Sets

function: posterior_samples package: tidyposterior

Example Data Sets

function: contrast_samples package: tidyposterior

Example Objects

function: example_data package: stacks

Example Objects

function: reg_res_svm package: stacks

Example Objects

function: reg_res_sp package: stacks

Example Objects

function: reg_res_lr package: stacks

Example Objects

function: reg_folds package: stacks

Example Objects

function: class_res_nn package: stacks

Example Objects

function: class_res_rf package: stacks

Example Objects

function: class_folds package: stacks

Example Objects

function: log_res_nn package: stacks

Example Objects

function: log_res_rf package: stacks

Example Objects

function: tree_frogs_reg_test package: stacks

Example Objects

function: tree_frogs_class_test package: stacks

Example bivariate classification data

function: bivariate package: modeldata

Example bivariate classification data

function: bivariate_train package: modeldata

Example bivariate classification data

function: bivariate_test package: modeldata

Example bivariate classification data

function: bivariate_val package: modeldata

Example data for hardhat

function: hardhat-example-data package: hardhat

Example data for hardhat

function: example_train package: hardhat

Example data for hardhat

function: example_test package: hardhat

Execution time data

function: check_times package: modeldata

Execution-time data dimension checks

function: min_cols package: parsnip

Execution-time data dimension checks

function: min_rows package: parsnip

Explore model results

function: explore.default package: shinymodels

Explore model results

function: explore.tune_results package: shinymodels

Exponential decay function

function: expo_decay package: tune

Extend case weights

function: new_case_weights package: hardhat

Extending rsample with new rset subclasses

function: rset_reconstruct package: rsample

Extract Model Coefficients

function: brulee-coefs package: brulee

Extract Model Coefficients

function: coef.brulee_logistic_reg package: brulee

Extract Model Coefficients

function: coef.brulee_linear_reg package: brulee

Extract Model Coefficients

function: coef.brulee_mlp package: brulee

Extract Model Coefficients

function: coef.brulee_multinomial_reg package: brulee

Extract Posterior Distributions for Models

function: tidy.perf_mod package: tidyposterior

Extract Predictor Names from Formula or Terms

function: form_pred package: rsample

Extract a model offset

function: model_offset package: hardhat

Extract class_pred levels

function: levels.class_pred package: probably

Extract cluster assignments from model

function: extract_cluster_assignment package: tidyclust

Extract clusters from model

function: extract_centroids package: tidyclust

Extract data classes from a data frame or matrix

function: get_data_classes package: hardhat

Extract elements from a workflow set

function: pull_workflow_set_result package: workflowsets

Extract elements from a workflow set

function: pull_workflow package: workflowsets

Extract elements of a parsnip model object

function: extract-parsnip package: parsnip

Extract elements of a parsnip model object

function: extract_spec_parsnip.model_fit package: parsnip

Extract elements of a parsnip model object

function: extract_fit_engine.model_fit package: parsnip

Extract elements of a parsnip model object

function: extract_parameter_set_dials.model_spec package: parsnip

Extract elements of a parsnip model object

function: extract_parameter_dials.model_spec package: parsnip

Extract elements of a parsnip model object

function: extract_fit_time.model_fit package: parsnip

Extract elements of a tidyclust model object

function: extract-tidyclust package: tidyclust

Extract elements of a tidyclust model object

function: extract_fit_engine.cluster_fit package: tidyclust

Extract elements of a tidyclust model object

function: extract_parameter_set_dials.cluster_spec package: tidyclust

Extract elements of a workflow

function: extract-workflow package: workflows

Extract elements of a workflow

function: extract_spec_parsnip.workflow package: workflows

Extract elements of a workflow

function: extract_recipe.workflow package: workflows

Extract elements of a workflow

function: extract_fit_parsnip.workflow package: workflows

Extract elements of a workflow

function: extract_fit_engine.workflow package: workflows

Extract elements of a workflow

function: extract_mold.workflow package: workflows

Extract elements of a workflow

function: extract_preprocessor.workflow package: workflows

Extract elements of a workflow

function: extract_postprocessor.workflow package: workflows

Extract elements of a workflow

function: extract_tailor.workflow package: workflows

Extract elements of a workflow

function: extract_parameter_set_dials.workflow package: workflows

Extract elements of a workflow

function: extract_parameter_dials.workflow package: workflows

Extract elements of a workflow

function: extract_fit_time.workflow package: workflows

Extract elements of tune objects

function: extract-tune package: tune

Extract elements of tune objects

function: extract_workflow.last_fit package: tune

Extract elements of tune objects

function: extract_workflow.tune_results package: tune

Extract elements of tune objects

function: extract_spec_parsnip.tune_results package: tune

Extract elements of tune objects

function: extract_recipe.tune_results package: tune

Extract elements of tune objects

function: extract_fit_parsnip.tune_results package: tune

Extract elements of tune objects

function: extract_fit_engine.tune_results package: tune

Extract elements of tune objects

function: extract_mold.tune_results package: tune

Extract elements of tune objects

function: extract_preprocessor.tune_results package: tune

Extract elements of workflow sets

function: extract_workflow_set_result package: workflowsets

Extract elements of workflow sets

function: extract_workflow.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_spec_parsnip.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_recipe.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_fit_parsnip.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_fit_engine.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_mold.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_preprocessor.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_parameter_set_dials.workflow_set package: workflowsets

Extract elements of workflow sets

function: extract_parameter_dials.workflow_set package: workflowsets

Extract factor levels from a data frame

function: get_levels package: hardhat

Extract factor levels from a data frame

function: get_outcome_levels package: hardhat

Extract patterns from nominal data

function: step_dummy_extract package: recipes

Extract resample weights from rset or tuning objects

function: extract_resample_weights package: tune

Extract transformed training set

function: juice package: recipes

F Measure

function: f_meas package: yardstick

F Measure

function: f_meas.data.frame package: yardstick

F Measure

function: f_meas_vec package: yardstick

Facilities for loading and updating other packages

function: tag_show package: tidymodels

Facilities for loading and updating other packages

function: tags package: tidymodels

Facilities for loading and updating other packages

function: tag_attach package: tidymodels

Facilities for loading and updating other packages

function: tag_update package: tidymodels

Fall-out (False Positive Rate)

function: fall_out package: yardstick

Fall-out (False Positive Rate)

function: fall_out.data.frame package: yardstick

Fall-out (False Positive Rate)

function: fall_out_vec package: yardstick

Fashion a correlation data frame for printing.

function: fashion package: corrr

Fat, water and protein content of meat samples

function: meats package: modeldata

Fatty acid composition of commercial oils

function: oils package: modeldata

Feature Hashing of Tokens

function: step_texthash package: textrecipes

Feature Hashing of Tokens

function: tidy.step_texthash package: textrecipes

Fill safe value (singular)

function: fill_safe_value package: filtro

Fill safe values (plural)

function: fill_safe_values package: filtro

Filter Tokens Based on Term Frequency

function: step_tokenfilter package: textrecipes

Filter Tokens Based on Term Frequency

function: tidy.step_tokenfilter package: textrecipes

Filter rows by position using dplyr

function: step_slice package: recipes

Filter rows using dplyr

function: step_filter package: recipes

Filtering of Stop Words for Tokens Variables

function: step_stopwords package: textrecipes

Filtering of Stop Words for Tokens Variables

function: tidy.step_stopwords package: textrecipes

Find Labels from rset Object

function: labels.rset package: rsample

Find Labels from rset Object

function: labels.vfold_cv package: rsample

Find Labels from rsplit Object

function: labels.rsplit package: rsample

Fine foods example data

function: small_fine_foods package: modeldata

Fine foods example data

function: training_data package: modeldata

Fine foods example data

function: testing_data package: modeldata

Fit a Model Specification to a Data Set

function: fit.cluster_spec package: tidyclust

Fit a Model Specification to a Data Set

function: fit_xy.cluster_spec package: tidyclust

Fit a Model Specification to a Dataset

function: fit.model_spec package: parsnip

Fit a Model Specification to a Dataset

function: fit_xy.model_spec package: parsnip

Fit a apd_hat_values

function: apd_hat_values package: applicable

Fit a apd_hat_values

function: apd_hat_values.default package: applicable

Fit a apd_hat_values

function: apd_hat_values.data.frame package: applicable

Fit a apd_hat_values

function: apd_hat_values.matrix package: applicable

Fit a apd_hat_values

function: apd_hat_values.formula package: applicable

Fit a apd_hat_values

function: apd_hat_values.recipe package: applicable

Fit a apd_pca

function: apd_pca package: applicable

Fit a apd_pca

function: apd_pca.default package: applicable

Fit a apd_pca

function: apd_pca.data.frame package: applicable

Fit a apd_pca

function: apd_pca.matrix package: applicable

Fit a apd_pca

function: apd_pca.formula package: applicable

Fit a apd_pca

function: apd_pca.recipe package: applicable

Fit a grouped binomial outcome from a data set with case weights

function: glm_grouped package: parsnip

Fit a linear regression model

function: brulee_linear_reg package: brulee

Fit a linear regression model

function: brulee_linear_reg.default package: brulee

Fit a linear regression model

function: brulee_linear_reg.data.frame package: brulee

Fit a linear regression model

function: brulee_linear_reg.matrix package: brulee

Fit a linear regression model

function: brulee_linear_reg.formula package: brulee

Fit a linear regression model

function: brulee_linear_reg.recipe package: brulee

Fit a logistic regression model

function: brulee_logistic_reg package: brulee

Fit a logistic regression model

function: brulee_logistic_reg.default package: brulee

Fit a logistic regression model

function: brulee_logistic_reg.data.frame package: brulee

Fit a logistic regression model

function: brulee_logistic_reg.matrix package: brulee

Fit a logistic regression model

function: brulee_logistic_reg.formula package: brulee

Fit a logistic regression model

function: brulee_logistic_reg.recipe package: brulee

Fit a model to the numerically optimal configuration

function: fit_best package: tune

Fit a model to the numerically optimal configuration

function: fit_best.default package: tune

Fit a model to the numerically optimal configuration

function: fit_best.tune_results package: tune

Fit a model to the numerically optimal configuration

function: fit_best.workflow_set package: workflowsets

Fit a multinomial regression model

function: brulee_multinomial_reg package: brulee

Fit a multinomial regression model

function: brulee_multinomial_reg.default package: brulee

Fit a multinomial regression model

function: brulee_multinomial_reg.data.frame package: brulee

Fit a multinomial regression model

function: brulee_multinomial_reg.matrix package: brulee

Fit a multinomial regression model

function: brulee_multinomial_reg.formula package: brulee

Fit a multinomial regression model

function: brulee_multinomial_reg.recipe package: brulee

Fit a workflow object

function: fit-workflow package: workflows

Fit a workflow object

function: fit.workflow package: workflows

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation package: applicable

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation.default package: applicable

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation.data.frame package: applicable

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation.matrix package: applicable

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation.formula package: applicable

Fit an isolation forest to estimate an applicability domain.

function: apd_isolation.recipe package: applicable

Fit linear models to infer objects

function: fit.infer package: infer

Fit model stack members with non-zero stacking coefficients

function: fit_members package: stacks

Fit multiple models via resampling

function: fit_resamples package: tune

Fit multiple models via resampling

function: fit_resamples.model_spec package: tune

Fit multiple models via resampling

function: fit_resamples.workflow package: tune

Fit neural networks

function: brulee_mlp package: brulee

Fit neural networks

function: brulee_mlp.default package: brulee

Fit neural networks

function: brulee_mlp.data.frame package: brulee

Fit neural networks

function: brulee_mlp.matrix package: brulee

Fit neural networks

function: brulee_mlp.formula package: brulee

Fit neural networks

function: brulee_mlp.recipe package: brulee

Fit neural networks

function: brulee_mlp_two_layer package: brulee

Fit neural networks

function: brulee_mlp_two_layer.default package: brulee

Fit neural networks

function: brulee_mlp_two_layer.data.frame package: brulee

Fit neural networks

function: brulee_mlp_two_layer.matrix package: brulee

Fit neural networks

function: brulee_mlp_two_layer.formula package: brulee

Fit neural networks

function: brulee_mlp_two_layer.recipe package: brulee

Fit the final best model to the training set and evaluate the test set

function: last_fit package: tune

Fit the final best model to the training set and evaluate the test set

function: last_fit.model_spec package: tune

Fit the final best model to the training set and evaluate the test set

function: last_fit.workflow package: tune

Fits a Linear Regression model

function: linear_regression_db package: modeldb

Flexible discriminant analysis

function: discrim_flexible package: parsnip

Focus on section of a correlation data frame.

function: focus package: corrr

Focus on section of a correlation data frame.

function: focus_ package: corrr

Food Delivery Time Data

function: deliveries package: modeldata

Forge prediction-ready data

function: forge package: hardhat

Formulas with special terms in tidymodels

function: model_formula package: parsnip

Frequency weights

function: frequency_weights package: hardhat

Functions to create boilerplate code for specific models

function: use_glmnet package: usemodels

Functions to create boilerplate code for specific models

function: use_xgboost package: usemodels

Functions to create boilerplate code for specific models

function: use_kknn package: usemodels

Functions to create boilerplate code for specific models

function: use_ranger package: usemodels

Functions to create boilerplate code for specific models

function: use_earth package: usemodels

Functions to create boilerplate code for specific models

function: use_cubist package: usemodels

Functions to create boilerplate code for specific models

function: use_kernlab_svm_rbf package: usemodels

Functions to create boilerplate code for specific models

function: use_kernlab_svm_poly package: usemodels

Functions to create boilerplate code for specific models

function: use_C5.0 package: usemodels

Functions to finalize data-specific parameter ranges

function: finalize package: dials

Functions to finalize data-specific parameter ranges

function: finalize.list package: dials

Functions to finalize data-specific parameter ranges

function: finalize.param package: dials

Functions to finalize data-specific parameter ranges

function: finalize.parameters package: dials

Functions to finalize data-specific parameter ranges

function: finalize.logical package: dials

Functions to finalize data-specific parameter ranges

function: finalize.default package: dials

Functions to finalize data-specific parameter ranges

function: get_p package: dials

Functions to finalize data-specific parameter ranges

function: get_log_p package: dials

Functions to finalize data-specific parameter ranges

function: get_n_frac package: dials

Functions to finalize data-specific parameter ranges

function: get_n_frac_range package: dials

Functions to finalize data-specific parameter ranges

function: get_n package: dials

Functions to finalize data-specific parameter ranges

function: get_rbf_range package: dials

Fuzzy conversions

function: maybe_matrix package: parsnip

Fuzzy conversions

function: maybe_data_frame package: parsnip

Gain capture

function: gain_capture package: yardstick

Gain capture

function: gain_capture.data.frame package: yardstick

Gain capture

function: gain_capture_vec package: yardstick

Gain curve

function: gain_curve package: yardstick

Gain curve

function: gain_curve.data.frame package: yardstick

Gaussian Mixture Models (GMM)

function: gm_clust package: tidyclust

Gaussian mixture covariance structure parameters

function: gm_clust_params package: tidyclust

Gaussian mixture covariance structure parameters

function: circular package: tidyclust

Gaussian mixture covariance structure parameters

function: zero_covariance package: tidyclust

Gaussian mixture covariance structure parameters

function: shared_orientation package: tidyclust

Gaussian mixture covariance structure parameters

function: shared_shape package: tidyclust

Gaussian mixture covariance structure parameters

function: shared_size package: tidyclust

General Function to Estimate Performance

function: metrics package: yardstick

General Function to Estimate Performance

function: metrics.data.frame package: yardstick

General thresholding parameter

function: threshold package: dials

General variable filter

function: step_rm package: recipes

Generalized additive models (GAMs)

function: gen_additive_mod package: parsnip

Generalized bernstein polynomial basis

function: step_poly_bernstein package: recipes

Generate a set of workflow objects from preprocessing and model objects

function: workflow_set package: workflowsets

Generate n-grams From Token Variables

function: step_ngram package: textrecipes

Generate n-grams From Token Variables

function: tidy.step_ngram package: textrecipes

Generate performance metrics across probability thresholds

function: threshold_perf package: probably

Generate performance metrics across probability thresholds

function: threshold_perf.data.frame package: probably

Generate resamples, permutations, or simulations

function: generate package: infer

Generics for object extraction

function: hardhat-extract package: hardhat

Generics for object extraction

function: extract_workflow package: hardhat

Generics for object extraction

function: extract_recipe package: hardhat

Generics for object extraction

function: extract_spec_parsnip package: hardhat

Generics for object extraction

function: extract_fit_parsnip package: hardhat

Generics for object extraction

function: extract_fit_engine package: hardhat

Generics for object extraction

function: extract_mold package: hardhat

Generics for object extraction

function: extract_preprocessor package: hardhat

Generics for object extraction

function: extract_postprocessor package: hardhat

Generics for object extraction

function: extract_tailor package: hardhat

Generics for object extraction

function: extract_parameter_dials package: hardhat

Generics for object extraction

function: extract_parameter_set_dials package: hardhat

Generics for object extraction

function: extract_fit_time package: hardhat

Get all metrics of a given type

function: get_metrics package: yardstick

Give object unsupervised fit class

function: new_unsupervised_fit package: modelenv

Give object unsupervised specification class

function: new_unsupervised_spec package: modelenv

Glance at a workflow model

function: glance.workflow package: workflows

Glance at a(n) Arima object

function: glance.Arima package: broom

Glance at a(n) Gam object

function: glance.Gam package: broom

Glance at a(n) Mclust object

function: glance.Mclust package: broom

Glance at a(n) aareg object

function: glance.aareg package: broom

Glance at a(n) anova object

function: glance.anova package: broom

Glance at a(n) betamfx object

function: glance.betamfx package: broom

Glance at a(n) betareg object

function: glance.betareg package: broom

Glance at a(n) biglm object

function: glance.biglm package: broom

Glance at a(n) binDesign object

function: glance.binDesign package: broom

Glance at a(n) cch object

function: glance.cch package: broom

Glance at a(n) clm object

function: glance.clm package: broom

Glance at a(n) clmm object

function: glance.clmm package: broom

Glance at a(n) coeftest object

function: glance.coeftest package: broom

Glance at a(n) coxph object

function: glance.coxph package: broom

Glance at a(n) crr object

function: glance.crr package: broom

Glance at a(n) cv.glmnet object

function: glance.cv.glmnet package: broom

Glance at a(n) drc object

function: glance.drc package: broom

Glance at a(n) ergm object

function: glance.ergm package: broom

Glance at a(n) factanal object

function: glance.factanal package: broom

Glance at a(n) felm object

function: glance.felm package: broom

Glance at a(n) fitdistr object

function: glance.fitdistr package: broom

Glance at a(n) fixest object

function: glance.fixest package: broom

Glance at a(n) gam object

function: glance.gam package: broom

Glance at a(n) geeglm object

function: glance.geeglm package: broom

Glance at a(n) glm object

function: glance.glm package: broom

Glance at a(n) glmRob object

function: glance.glmRob package: broom

Glance at a(n) glmnet object

function: glance.glmnet package: broom

Glance at a(n) gmm object

function: glance.gmm package: broom

Glance at a(n) ivreg object

function: glance.ivreg package: broom

Glance at a(n) kmeans object

function: glance.kmeans package: broom

Glance at a(n) lavaan object

function: glance.lavaan package: broom

Glance at a(n) lm object

function: glance.aov package: broom

Glance at a(n) lm object

function: glance.lm package: broom

Glance at a(n) lmRob object

function: glance.lmRob package: broom

Glance at a(n) lmodel2 object

function: glance.lmodel2 package: broom

Glance at a(n) lmrob object

function: glance.lmrob package: broom

Glance at a(n) margins object

function: glance.margins package: broom

Glance at a(n) mfx object

function: glance.mfx package: broom

Glance at a(n) mfx object

function: glance.logitmfx package: broom

Glance at a(n) mfx object

function: glance.negbinmfx package: broom

Glance at a(n) mfx object

function: glance.poissonmfx package: broom

Glance at a(n) mfx object

function: glance.probitmfx package: broom

Glance at a(n) mjoint object

function: glance.mjoint package: broom

Glance at a(n) mlogit object

function: glance.mlogit package: broom

Glance at a(n) muhaz object

function: glance.muhaz package: broom

Glance at a(n) multinom object

function: glance.multinom package: broom

Glance at a(n) negbin object

function: glance.negbin package: broom

Glance at a(n) negbin object

function: glm.nb_tidiers package: broom

Glance at a(n) nlrq object

function: glance.nlrq package: broom

Glance at a(n) nls object

function: glance.nls package: broom

Glance at a(n) pam object

function: glance.pam package: broom

Glance at a(n) plm object

function: glance.plm package: broom

Glance at a(n) poLCA object

function: glance.poLCA package: broom

Glance at a(n) polr object

function: glance.polr package: broom

Glance at a(n) pyears object

function: glance.pyears package: broom

Glance at a(n) ridgelm object

function: glance.ridgelm package: broom

Glance at a(n) rlm object

function: glance.rlm package: broom

Glance at a(n) rlm object

function: rlm_tidiers package: broom

Glance at a(n) rma object

function: glance.rma package: broom

Glance at a(n) rq object

function: glance.rq package: broom

Glance at a(n) spatialreg object

function: glance.sarlm package: broom

Glance at a(n) speedglm object

function: glance.speedglm package: broom

Glance at a(n) speedlm object

function: glance.speedlm package: broom

Glance at a(n) summary.lm object

function: glance.summary.lm package: broom

Glance at a(n) survdiff object

function: glance.survdiff package: broom

Glance at a(n) survexp object

function: glance.survexp package: broom

Glance at a(n) survfit object

function: glance.survfit package: broom

Glance at a(n) survreg object

function: glance.survreg package: broom

Glance at a(n) svyglm object

function: glance.svyglm package: broom

Glance at a(n) svyolr object

function: glance.svyolr package: broom

Glance at a(n) varest object

function: glance.varest package: broom

Gradient descent momentum parameter

function: momentum package: dials

Grant acceptance data

function: grants package: modeldata

Grant acceptance data

function: grants_other package: modeldata

Grant acceptance data

function: grants_test package: modeldata

Grant acceptance data

function: grants_2008 package: modeldata

Group Bootstraps

function: group_bootstraps package: rsample

Group Monte Carlo Cross-Validation

function: group_mc_cv package: rsample

Group V-Fold Cross-Validation

function: group_vfold_cv package: rsample

Handle levels in multiple predictors together

function: step_dummy_multi_choice package: recipes

Harmonic Frequency

function: harmonic_frequency package: dials

Helpers for steps with case weights

function: case-weight-helpers package: recipes

Helpers for steps with case weights

function: get_case_weights package: recipes

Helpers for steps with case weights

function: averages package: recipes

Helpers for steps with case weights

function: medians package: recipes

Helpers for steps with case weights

function: variances package: recipes

Helpers for steps with case weights

function: correlations package: recipes

Helpers for steps with case weights

function: covariances package: recipes

Helpers for steps with case weights

function: pca_wts package: recipes

Helpers for steps with case weights

function: are_weights_used package: recipes

Hierarchical (Agglomerative) Clustering

function: hier_clust package: tidyclust

High correlation filter

function: step_corr package: recipes

High-performance computing system data

function: hpc_data package: modeldata

Holiday feature generator

function: step_holiday package: recipes

Huber loss

function: huber_loss package: yardstick

Huber loss

function: huber_loss.data.frame package: yardstick

Huber loss

function: huber_loss_vec package: yardstick

Hyperbolic transformations

function: step_hyperbolic package: recipes

ICA signal extraction

function: step_ica package: recipes

Image segmentation predictions

function: segment_naive_bayes package: probably

Image segmentation predictions

function: segment_logistic package: probably

Imipramine longitudinal data

function: riesby package: multilevelmod

Importance weights

function: importance_weights package: hardhat

Impute additional quantiles from a quantile_pred

function: impute_quantiles package: hardhat

Impute nominal data using the most common value

function: step_impute_mode package: recipes

Impute numeric data below the threshold of measurement

function: step_impute_lower package: recipes

Impute numeric data using a rolling window statistic

function: step_impute_roll package: recipes

Impute numeric data using the mean

function: step_impute_mean package: recipes

Impute numeric data using the median

function: step_impute_median package: recipes

Impute numeric variables via a linear model

function: step_impute_linear package: recipes

Impute via bagged trees

function: step_impute_bag package: recipes

Impute via bagged trees

function: imp_vars package: recipes

Impute via k-nearest neighbors

function: step_impute_knn package: recipes

Index of ideality of correlation

function: iic package: yardstick

Index of ideality of correlation

function: iic.data.frame package: yardstick

Index of ideality of correlation

function: iic_vec package: yardstick

Indicator Variables via Feature Hashing

function: step_dummy_hash package: textrecipes

Indicator Variables via Feature Hashing

function: tidy.step_dummy_hash package: textrecipes

Initialization method for UMAP

function: initial_umap package: dials

Initialization method for UMAP

function: values_initial_umap package: dials

Initialize a Stack

function: stacks package: stacks

Integrated Brier score for right censored data

function: brier_survival_integrated package: yardstick

Integrated Brier score for right censored data

function: brier_survival_integrated.data.frame package: yardstick

Integrated Brier score for right censored data

function: brier_survival_integrated_vec package: yardstick

Inverse logit transformation

function: step_invlogit package: recipes

Inverse transformation

function: step_inverse package: recipes

Investigate best tuning parameters

function: show_best.tune_race package: finetune

Investigate best tuning parameters

function: show_best package: tune

Investigate best tuning parameters

function: show_best.default package: tune

Investigate best tuning parameters

function: show_best.tune_results package: tune

Investigate best tuning parameters

function: select_best package: tune

Investigate best tuning parameters

function: select_best.default package: tune

Investigate best tuning parameters

function: select_best.tune_results package: tune

Investigate best tuning parameters

function: select_by_pct_loss package: tune

Investigate best tuning parameters

function: select_by_pct_loss.default package: tune

Investigate best tuning parameters

function: select_by_pct_loss.tune_results package: tune

Investigate best tuning parameters

function: select_by_one_std_err package: tune

Investigate best tuning parameters

function: select_by_one_std_err.default package: tune

Investigate best tuning parameters

function: select_by_one_std_err.tune_results package: tune

Is x a case weights vector?

function: is_case_weights package: hardhat

Is x a frequency weights vector?

function: is_frequency_weights package: hardhat

Is x a preprocessing blueprint?

function: is_blueprint package: hardhat

Is x an importance weights vector?

function: is_importance_weights package: hardhat

Isomap embedding

function: step_isomap package: recipes

Iterative optimization of neural network

function: ames_mlp_itr package: shinymodels

J-index

function: j_index package: yardstick

J-index

function: j_index.data.frame package: yardstick

J-index

function: j_index_vec package: yardstick

Job attrition

function: attrition package: modeldata

K-Means

function: k_means package: tidyclust

K-nearest neighbors

function: nearest_neighbor package: parsnip

Kappa

function: kap package: yardstick

Kappa

function: kap.data.frame package: yardstick

Kappa

function: kap_vec package: yardstick

Kelly Blue Book resale data for 2005 model year GM cars

function: car_prices package: modeldata

Kernel PCA signal extraction

function: step_kpca package: recipes

Kernel Smoothness

function: smoothness package: dials

Kernel functions for distance weighting

function: weight_func package: dials

Kernel functions for distance weighting

function: values_weight_func package: dials

Kernel parameters

function: rbf_sigma package: dials

Kernel parameters

function: scale_factor package: dials

Kernel parameters

function: kernel_offset package: dials

Laplace correction parameter

function: Laplace package: dials

Leaf identification data (Flavia)

function: leaf_id_flavia package: modeldata

Learning rate

function: learn_rate package: dials

Leave-One-Out Cross-Validation

function: loo_cv package: rsample

Lemmatization of Token Variables

function: step_lemma package: textrecipes

Lemmatization of Token Variables

function: tidy.step_lemma package: textrecipes

Lift curve

function: lift_curve package: yardstick

Lift curve

function: lift_curve.data.frame package: yardstick

Likelihood encoding using analytical formula

function: step_lencode package: embed

Likelihood encoding using analytical formula

function: tidy.step_lencode package: embed

Limits for the range of predictions

function: range_limits package: dials

Limits for the range of predictions

function: lower_limit package: dials

Limits for the range of predictions

function: upper_limit package: dials

Linear combination filter

function: step_lincomb package: recipes

Linear discriminant analysis

function: discrim_linear package: parsnip

Linear regression

function: linear_reg package: parsnip

Linear support vector machines

function: svm_linear package: parsnip

List all dependencies

function: pkg_deps package: tidymodels

List all packages in the tidymodels

function: tidymodels_packages package: tidymodels

List of all feature counting functions

function: count_functions package: textrecipes

Liver Pathology Data

function: pathology package: yardstick

Liver pathology data

function: pathology package: modeldata

Loan data

function: lending_club package: modeldata

Locate equivocal values

function: locate-equivocal package: probably

Locate equivocal values

function: is_equivocal package: probably

Locate equivocal values

function: which_equivocal package: probably

Locate equivocal values

function: any_equivocal package: probably

Locate part of an object.

function: locate package: butcher

Logarithmic transformation

function: step_log package: recipes

Logistic regression

function: logistic_reg package: parsnip

Logit transformation

function: step_logit package: recipes

MARS pruning methods

function: prune_method package: dials

MARS pruning methods

function: values_prune_method package: dials

Make a classed list of options

function: option_list package: workflowsets

Manual resampling

function: manual_rset package: rsample

Manually alter roles

function: roles package: recipes

Manually alter roles

function: add_role package: recipes

Manually alter roles

function: update_role package: recipes

Manually alter roles

function: remove_role package: recipes

Mark arguments for tuning

function: tune package: hardhat

Markedness

function: markedness package: yardstick

Markedness

function: markedness.data.frame package: yardstick

Markedness

function: markedness_vec package: yardstick

Matthews correlation coefficient

function: mcc package: yardstick

Matthews correlation coefficient

function: mcc.data.frame package: yardstick

Matthews correlation coefficient

function: mcc_vec package: yardstick

Maximum number of retained tokens

function: max_tokens package: dials

Mean Shift Clustering

function: mean_shift package: tidyclust

Mean absolute error

function: mae package: yardstick

Mean absolute error

function: mae.data.frame package: yardstick

Mean absolute error

function: mae_vec package: yardstick

Mean absolute percent error

function: mape package: yardstick

Mean absolute percent error

function: mape.data.frame package: yardstick

Mean absolute percent error

function: mape_vec package: yardstick

Mean absolute scaled error

function: mase package: yardstick

Mean absolute scaled error

function: mase.data.frame package: yardstick

Mean absolute scaled error

function: mase_vec package: yardstick

Mean log loss for Poisson data

function: poisson_log_loss package: yardstick

Mean log loss for Poisson data

function: poisson_log_loss.data.frame package: yardstick

Mean log loss for Poisson data

function: poisson_log_loss_vec package: yardstick

Mean log loss for multinomial data

function: mn_log_loss package: yardstick

Mean log loss for multinomial data

function: mn_log_loss.data.frame package: yardstick

Mean log loss for multinomial data

function: mn_log_loss_vec package: yardstick

Mean percentage error

function: mpe package: yardstick

Mean percentage error

function: mpe.data.frame package: yardstick

Mean percentage error

function: mpe_vec package: yardstick

Mean signed deviation

function: msd package: yardstick

Mean signed deviation

function: msd.data.frame package: yardstick

Mean signed deviation

function: msd_vec package: yardstick

Mean squared error

function: mse package: yardstick

Mean squared error

function: mse.data.frame package: yardstick

Mean squared error

function: mse_vec package: yardstick

Measure performance with and without using Beta calibration

function: cal_validate_beta package: probably

Measure performance with and without using Beta calibration

function: cal_validate_beta.resample_results package: probably

Measure performance with and without using Beta calibration

function: cal_validate_beta.rset package: probably

Measure performance with and without using Beta calibration

function: cal_validate_beta.tune_results package: probably

Measure performance with and without using bagged isotonic regression calibration

function: cal_validate_isotonic_boot package: probably

Measure performance with and without using bagged isotonic regression calibration

function: cal_validate_isotonic_boot.resample_results package: probably

Measure performance with and without using bagged isotonic regression calibration

function: cal_validate_isotonic_boot.rset package: probably

Measure performance with and without using bagged isotonic regression calibration

function: cal_validate_isotonic_boot.tune_results package: probably

Measure performance with and without using isotonic regression calibration

function: cal_validate_isotonic package: probably

Measure performance with and without using isotonic regression calibration

function: cal_validate_isotonic.resample_results package: probably

Measure performance with and without using isotonic regression calibration

function: cal_validate_isotonic.rset package: probably

Measure performance with and without using isotonic regression calibration

function: cal_validate_isotonic.tune_results package: probably

Measure performance with and without using linear regression calibration

function: cal_validate_linear package: probably

Measure performance with and without using linear regression calibration

function: cal_validate_linear.resample_results package: probably

Measure performance with and without using linear regression calibration

function: cal_validate_linear.rset package: probably

Measure performance with and without using logistic calibration

function: cal_validate_logistic package: probably

Measure performance with and without using logistic calibration

function: cal_validate_logistic.resample_results package: probably

Measure performance with and without using logistic calibration

function: cal_validate_logistic.rset package: probably

Measure performance with and without using logistic calibration

function: cal_validate_logistic.tune_results package: probably

Measure performance with and without using multinomial calibration

function: cal_validate_multinomial package: probably

Measure performance with and without using multinomial calibration

function: cal_validate_multinomial.resample_results package: probably

Measure performance with and without using multinomial calibration

function: cal_validate_multinomial.rset package: probably

Measure performance with and without using multinomial calibration

function: cal_validate_multinomial.tune_results package: probably

Measure performance without using calibration

function: cal_validate_none package: probably

Measure performance without using calibration

function: cal_validate_none.resample_results package: probably

Measure performance without using calibration

function: cal_validate_none.rset package: probably

Measure performance without using calibration

function: cal_validate_none.tune_results package: probably

Measurement systems analysis data

function: msa_data package: multilevelmod

Measures average silhouette across all observations

function: silhouette_avg package: tidyclust

Measures average silhouette across all observations

function: silhouette_avg.cluster_spec package: tidyclust

Measures average silhouette across all observations

function: silhouette_avg.cluster_fit package: tidyclust

Measures average silhouette across all observations

function: silhouette_avg.workflow package: tidyclust

Measures average silhouette across all observations

function: silhouette_avg_vec package: tidyclust

Measures silhouette between clusters

function: silhouette package: tidyclust

Methods for model calibration

function: cal_method_class package: dials

Methods for model calibration

function: cal_method_reg package: dials

Methods for model calibration

function: values_cal_cls package: dials

Methods for model calibration

function: values_cal_reg package: dials

Methods for selecting variables in step functions

function: selections package: recipes

Methods for selecting variables in step functions

function: selection package: recipes

Minimum number of points

function: min_points package: tidyclust

Minkowski distance parameter

function: dist_power package: dials

Miss rate (False Negative Rate)

function: miss_rate package: yardstick

Miss rate (False Negative Rate)

function: miss_rate.data.frame package: yardstick

Miss rate (False Negative Rate)

function: miss_rate_vec package: yardstick

Missing value column filter

function: step_filter_missing package: recipes

Mixture of penalization terms

function: mixture package: dials

Model Fit Object Information

function: cluster_fit package: tidyclust

Model Fit Objects

function: model_fit package: parsnip

Model Specification Information

function: cluster_spec package: tidyclust

Model Specifications

function: model_spec package: parsnip

Model predictions

function: predict.cluster_fit package: tidyclust

Model predictions

function: predict_raw.cluster_fit package: tidyclust

Model predictions across many sub-models

function: multi_predict package: parsnip

Model predictions across many sub-models

function: multi_predict.default package: parsnip

Model predictions across many sub-models

function: multi_predict._xgb.Booster package: parsnip

Model predictions across many sub-models

function: multi_predict._C5.0 package: parsnip

Model predictions across many sub-models

function: multi_predict._elnet package: parsnip

Model predictions across many sub-models

function: multi_predict._lognet package: parsnip

Model predictions across many sub-models

function: multi_predict._multnet package: parsnip

Model predictions across many sub-models

function: multi_predict._glmnetfit package: parsnip

Model predictions across many sub-models

function: multi_predict._earth package: parsnip

Model predictions across many sub-models

function: multi_predict._torch_mlp package: parsnip

Model predictions across many sub-models

function: multi_predict._train.kknn package: parsnip

Model predictions across many sub-models

function: multi_predict._mixo_pls package: plsmod

Model predictions across many sub-models

function: multi_predict._mixo_spls package: plsmod

Model predictions across many sub-models

function: multi_predict._mixo_plsda package: plsmod

Model predictions across many sub-models

function: multi_predict._mixo_splsda package: plsmod

Model tuning via grid search

function: tune_cluster package: tidyclust

Model tuning via grid search

function: tune_cluster.cluster_spec package: tidyclust

Model tuning via grid search

function: tune_cluster.workflow package: tidyclust

Model tuning via grid search

function: tune_grid package: tune

Model tuning via grid search

function: tune_grid.model_spec package: tune

Model tuning via grid search

function: tune_grid.workflow package: tune

Model wrappers for h2o

function: h2o_train package: agua

Model wrappers for h2o

function: h2o_train_rf package: agua

Model wrappers for h2o

function: h2o_train_xgboost package: agua

Model wrappers for h2o

function: h2o_train_gbm package: agua

Model wrappers for h2o

function: h2o_train_glm package: agua

Model wrappers for h2o

function: h2o_train_nb package: agua

Model wrappers for h2o

function: h2o_train_mlp package: agua

Model wrappers for h2o

function: h2o_train_rule package: agua

Model wrappers for h2o

function: h2o_train_auto package: agua

Mold data for modeling

function: mold package: hardhat

Monotone splines

function: step_spline_monotone package: recipes

Monte Carlo Cross-Validation

function: mc_cv package: rsample

Morphometric data on scat

function: scat package: modeldata

Moving window functions

function: step_window package: recipes

Multiclass Probability Predictions

function: hpc_cv package: yardstick

Multinomial regression

function: multinom_reg package: parsnip

Multivariate adaptive regression splines (MARS)

function: mars package: parsnip

Mutate multiple columns using dplyr

function: step_mutate_at package: recipes

Naive Bayes models

function: naive_Bayes package: parsnip

Naming Tools

function: names0 package: recipes

Naming Tools

function: dummy_names package: recipes

Naming Tools

function: dummy_extract_names package: recipes

Natural spline basis functions

function: step_ns package: recipes

Natural splines

function: step_spline_natural package: recipes

Near-zero variance filter

function: step_nzv package: recipes

Near-zero variance parameters

function: freq_cut package: dials

Near-zero variance parameters

function: unique_cut package: dials

Nearest neighbor distance matching (NNDM) cross-validation

function: spatial_nndm_cv package: spatialsample

Negative predictive value

function: npv package: yardstick

Negative predictive value

function: npv.data.frame package: yardstick

Negative predictive value

function: npv_vec package: yardstick

Nested or Double Resampling

function: nested_cv package: rsample

Network plot of a correlation data frame

function: network_plot package: corrr

Neural network parameters

function: dropout package: dials

Neural network parameters

function: epochs package: dials

Neural network parameters

function: hidden_units package: dials

Neural network parameters

function: hidden_units_2 package: dials

Neural network parameters

function: batch_size package: dials

New axe functions for a modeling object.

function: new_model_butcher package: butcher

Non-negative matrix factorization signal extraction with lasso penalization

function: step_nnmf_sparse package: recipes

Non-negative splines

function: step_spline_nonnegative package: recipes

Normalization of Character Variables

function: step_text_normalization package: textrecipes

Normalization of Character Variables

function: tidy.step_text_normalization package: textrecipes

Normalized Gini coefficient

function: gini_coef package: yardstick

Normalized Gini coefficient

function: gini_coef.data.frame package: yardstick

Normalized Gini coefficient

function: gini_coef_vec package: yardstick

Null model

function: null_model package: parsnip

Number of Clusters

function: num_clusters package: dials

Number of Computation Runs

function: num_runs package: dials

Number of cut-points for binning

function: num_breaks package: dials

Number of days before a movie grosses $1M USD

function: time_to_million package: censored

Number of knots (integer)

function: num_knots package: dials

Number of neighbors

function: neighbors package: dials

Number of new features

function: num_comp package: dials

Number of new features

function: num_terms package: dials

Number of pairwise complete cases.

function: pair_n package: corrr

Number of randomly sampled predictors

function: mtry package: dials

Number of randomly sampled predictors

function: mtry_long package: dials

Number of tokens in vocabulary

function: vocabulary_size package: dials

Number of unique values for pre-processing

function: min_unique package: dials

Obtain and format metrics produced by calibration validation

function: collect_metrics.cal_rset package: probably

Obtain and format predictions produced by calibration validation

function: collect_predictions.cal_rset package: probably

Obtain and format results produced by racing functions

function: collect_predictions package: finetune

Obtain and format results produced by racing functions

function: collect_predictions.tune_race package: finetune

Obtain and format results produced by racing functions

function: collect_metrics.tune_race package: finetune

Obtain and format results produced by tuning functions

function: collect_predictions package: tune

Obtain and format results produced by tuning functions

function: collect_predictions.default package: tune

Obtain and format results produced by tuning functions

function: collect_predictions.tune_results package: tune

Obtain and format results produced by tuning functions

function: collect_metrics package: tune

Obtain and format results produced by tuning functions

function: collect_metrics.tune_results package: tune

Obtain and format results produced by tuning functions

function: collect_notes package: tune

Obtain and format results produced by tuning functions

function: collect_notes.tune_results package: tune

Obtain and format results produced by tuning functions

function: collect_extracts package: tune

Obtain and format results produced by tuning functions

function: collect_extracts.tune_results package: tune

Obtain and format results produced by tuning functions for workflow sets

function: collect_metrics.workflow_set package: workflowsets

Obtain and format results produced by tuning functions for workflow sets

function: collect_predictions.workflow_set package: workflowsets

Obtain and format results produced by tuning functions for workflow sets

function: collect_notes.workflow_set package: workflowsets

Obtain and format results produced by tuning functions for workflow sets

function: collect_extracts.workflow_set package: workflowsets

Obtain prediction equations for all possible values of type

function: get_expressions package: stacks

Obtain prediction equations for all possible values of type

function: get_expressions._multnet package: stacks

Obtain prediction equations for all possible values of type

function: get_expressions._lognet package: stacks

Obtain prediction equations for all possible values of type

function: get_expressions._elnet package: stacks

Obtain variable importance scores

function: var_imp.bagger package: baguette

OkCupid Binary Predictors

function: okc_binary package: applicable

OkCupid Binary Predictors

function: okc_binary_train package: applicable

OkCupid Binary Predictors

function: okc_binary_test package: applicable

One-hot contrast matrix

function: contr_one_hot package: tidyclust

Optimization of model parameters via simulated annealing

function: tune_sim_anneal package: finetune

Optimization of model parameters via simulated annealing

function: tune_sim_anneal.model_spec package: finetune

Optimization of model parameters via simulated annealing

function: tune_sim_anneal.workflow package: finetune

Ordinal Regression Link Functions (character)

function: ordinal_link package: dials

Ordinal Regression Link Functions (character)

function: values_ordinal_link package: dials

Ordinal Regression Link Functions (character)

function: odds_link package: dials

Ordinal Regression Link Functions (character)

function: values_odds_link package: dials

Orthogonal polynomial basis functions

function: step_poly package: recipes

PCA signal extraction

function: step_pca package: recipes

Palmer Station penguin data

function: penguins package: modeldata

Parabolic class boundary data

function: parabolic package: discrim

Parabolic class boundary data

function: parabolic package: modeldata

Parameter for "double normalization" when creating token counts

function: weight package: dials

Parameter for the effective minimum distance between embedded points

function: min_dist package: dials

Parameter for the moving window size

function: window_size package: dials

Parameter functions for Cubist models

function: committees package: rules

Parameter functions for Cubist models

function: max_rules package: rules

Parameter functions related to tree- and rule-based models.

function: trees package: dials

Parameter functions related to tree- and rule-based models.

function: min_n package: dials

Parameter functions related to tree- and rule-based models.

function: sample_size package: dials

Parameter functions related to tree- and rule-based models.

function: sample_prop package: dials

Parameter functions related to tree- and rule-based models.

function: loss_reduction package: dials

Parameter functions related to tree- and rule-based models.

function: tree_depth package: dials

Parameter functions related to tree- and rule-based models.

function: prune package: dials

Parameter functions related to tree- and rule-based models.

function: cost_complexity package: dials

Parameter objects for Regularized Discriminant Models

function: frac_common_cov package: discrim

Parameter objects for Regularized Discriminant Models

function: frac_identity package: discrim

Parameter objects for Regularized Discriminant Models

function: smoothness package: discrim

Parameter to determine number of tokens in ngram

function: num_tokens package: dials

Parameter to determine which neighbors to use

function: all_neighbors package: dials

Parameter to enable feature selection

function: select_features package: dials

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

function: bart-param package: dials

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

function: prior_terminal_node_coef package: dials

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

function: prior_terminal_node_expo package: dials

Parameters for BART models These parameters are used for constructing Bayesian adaptive regression tree (BART) models.

function: prior_outcome_range package: dials

Parameters for TabPFN models

function: tab-pfn-param package: dials

Parameters for TabPFN models

function: num_estimators package: dials

Parameters for TabPFN models

function: softmax_temperature package: dials

Parameters for TabPFN models

function: balance_probabilities package: dials

Parameters for TabPFN models

function: average_before_softmax package: dials

Parameters for TabPFN models

function: training_set_limit package: dials

Parameters for class weights for imbalanced problems

function: class_weights package: dials

Parameters for class-imbalance sampling

function: over_ratio package: dials

Parameters for class-imbalance sampling

function: under_ratio package: dials

Parameters for exponents

function: degree package: dials

Parameters for exponents

function: degree_int package: dials

Parameters for exponents

function: spline_degree package: dials

Parameters for exponents

function: prod_degree package: dials

Parameters for neural network learning rate schedulers

function: scheduler-param package: dials

Parameters for neural network learning rate schedulers

function: rate_initial package: dials

Parameters for neural network learning rate schedulers

function: rate_largest package: dials

Parameters for neural network learning rate schedulers

function: rate_reduction package: dials

Parameters for neural network learning rate schedulers

function: rate_steps package: dials

Parameters for neural network learning rate schedulers

function: rate_step_size package: dials

Parameters for neural network learning rate schedulers

function: rate_decay package: dials

Parameters for neural network learning rate schedulers

function: rate_schedule package: dials

Parameters for neural network learning rate schedulers

function: values_scheduler package: dials

Parameters for possible engine parameters for C5.0

function: confidence_factor package: dials

Parameters for possible engine parameters for C5.0

function: no_global_pruning package: dials

Parameters for possible engine parameters for C5.0

function: predictor_winnowing package: dials

Parameters for possible engine parameters for C5.0

function: fuzzy_thresholding package: dials

Parameters for possible engine parameters for C5.0

function: rule_bands package: dials

Parameters for possible engine parameters for Cubist

function: extrapolation package: dials

Parameters for possible engine parameters for Cubist

function: unbiased_rules package: dials

Parameters for possible engine parameters for Cubist

function: max_rules package: dials

Parameters for possible engine parameters for earth models

function: max_num_terms package: dials

Parameters for possible engine parameters for partykit models

function: conditional_min_criterion package: dials

Parameters for possible engine parameters for partykit models

function: values_test_type package: dials

Parameters for possible engine parameters for partykit models

function: conditional_test_type package: dials

Parameters for possible engine parameters for partykit models

function: values_test_statistic package: dials

Parameters for possible engine parameters for partykit models

function: conditional_test_statistic package: dials

Parameters for possible engine parameters for randomForest

function: max_nodes package: dials

Parameters for possible engine parameters for ranger

function: regularization_factor package: dials

Parameters for possible engine parameters for ranger

function: regularize_depth package: dials

Parameters for possible engine parameters for ranger

function: significance_threshold package: dials

Parameters for possible engine parameters for ranger

function: lower_quantile package: dials

Parameters for possible engine parameters for ranger

function: splitting_rule package: dials

Parameters for possible engine parameters for ranger

function: ranger_class_rules package: dials

Parameters for possible engine parameters for ranger

function: ranger_reg_rules package: dials

Parameters for possible engine parameters for ranger

function: ranger_split_rules package: dials

Parameters for possible engine parameters for ranger

function: num_random_splits package: dials

Parameters for possible engine parameters for sda models

function: shrinkage_correlation package: dials

Parameters for possible engine parameters for sda models

function: shrinkage_variance package: dials

Parameters for possible engine parameters for sda models

function: shrinkage_frequencies package: dials

Parameters for possible engine parameters for sda models

function: diagonal_covariance package: dials

Parameters for possible engine parameters for xgboost

function: scale_pos_weight package: dials

Parameters for possible engine parameters for xgboost

function: penalty_L2 package: dials

Parameters for possible engine parameters for xgboost

function: penalty_L1 package: dials

Parameters to adjust effective degrees of freedom

function: adjust_deg_free package: dials

Parametric distributions for censored data

function: surv_dist package: dials

Parametric distributions for censored data

function: values_surv_dist package: dials

Parkinson's disease speech classification data set

function: pd_speech package: modeldata

Part of Speech Filtering of Token Variables

function: step_pos_filter package: textrecipes

Part of Speech Filtering of Token Variables

function: tidy.step_pos_filter package: textrecipes

Partial least squares (PLS)

function: pls package: parsnip

Partial least squares feature extraction

function: step_pls package: recipes

Percentile transformation

function: step_percentile package: recipes

Perform repeated sampling

function: rep_sample_n package: infer

Perform repeated sampling

function: rep_slice_sample package: infer

Permutation sampling

function: permutations package: rsample

Pipe

function: %>% package: infer

Placeholder for unknown parameter values

function: unknown package: dials

Placeholder for unknown parameter values

function: is_unknown package: dials

Placeholder for unknown parameter values

function: has_unknowns package: dials

Plot a correlation data frame.

function: rplot package: corrr

Plot model loss over epochs

function: brulee-autoplot package: brulee

Plot model loss over epochs

function: autoplot.brulee_mlp package: brulee

Plot model loss over epochs

function: autoplot.brulee_logistic_reg package: brulee

Plot model loss over epochs

function: autoplot.brulee_multinomial_reg package: brulee

Plot model loss over epochs

function: autoplot.brulee_linear_reg package: brulee

Plot racing results

function: plot_race package: finetune

Plot rankings and metrics of H2O AutoML results

function: autoplot.workflow package: agua

Plot rankings and metrics of H2O AutoML results

function: autoplot.H2OAutoML package: agua

Plot results of a stacked ensemble model.

function: autoplot.linear_stack package: stacks

Plot the cumulative distribution function for similarity metrics

function: autoplot.apd_similarity package: applicable

Plot the distribution function for principal components

function: autoplot.apd_pca package: applicable

Plot the results of a workflow set

function: autoplot.workflow_set package: workflowsets

Plot tuning search results

function: autoplot.tune_results package: tune

Poisson regression models

function: poisson_reg package: parsnip

Polynomial kernel PCA signal extraction

function: step_kpca_poly package: recipes

Polynomial support vector machines

function: svm_poly package: parsnip

Positional One-Hot encoding of Tokens

function: step_sequence_onehot package: textrecipes

Positional One-Hot encoding of Tokens

function: tidy.step_sequence_onehot package: textrecipes

Positive predictive value

function: ppv package: yardstick

Positive predictive value

function: ppv.data.frame package: yardstick

Positive predictive value

function: ppv_vec package: yardstick

Possible engine parameters for lightbgm

function: num_leaves package: dials

Precision

function: precision package: yardstick

Precision

function: precision.data.frame package: yardstick

Precision

function: precision_vec package: yardstick

Precision recall curve

function: pr_curve package: yardstick

Precision recall curve

function: pr_curve.data.frame package: yardstick

Predict from a apd_isolation

function: score.apd_isolation package: applicable

Predict from a apd_pca

function: score.apd_pca package: applicable

Predict from a brulee_linear_reg

function: predict.brulee_linear_reg package: brulee

Predict from a brulee_logistic_reg

function: predict.brulee_logistic_reg package: brulee

Predict from a brulee_mlp

function: predict.brulee_mlp package: brulee

Predict from a brulee_multinomial_reg

function: predict.brulee_multinomial_reg package: brulee

Predict from a workflow

function: predict-workflow package: workflows

Predict from a workflow

function: predict.workflow package: workflows

Predicting hepatic injury from chemical information

function: hepatic_injury_qsar package: modeldata

Predicting permeability from chemical information

function: permeability_qsar package: modeldata

Predicting steroidogenic toxicity with assay data

function: steroidogenic_toxicity package: modeldata

Predicting with a model stack

function: predict.data_stack package: stacks

Predicting with a model stack

function: predict.model_stack package: stacks

Prediction intervals from conformal methods

function: predict.int_conformal_cv package: probably

Prediction intervals from conformal methods

function: predict.int_conformal_full package: probably

Prediction intervals from conformal methods

function: predict.int_conformal_quantile package: probably

Prediction intervals from conformal methods

function: predict.int_conformal_split package: probably

Prediction intervals via conformal inference

function: int_conformal_full package: probably

Prediction intervals via conformal inference

function: int_conformal_full.default package: probably

Prediction intervals via conformal inference

function: int_conformal_full.workflow package: probably

Prediction intervals via conformal inference CV+

function: int_conformal_cv package: probably

Prediction intervals via conformal inference CV+

function: int_conformal_cv.default package: probably

Prediction intervals via conformal inference CV+

function: int_conformal_cv.resample_results package: probably

Prediction intervals via conformal inference CV+

function: int_conformal_cv.tune_results package: probably

Prediction intervals via conformal inference and quantile regression

function: int_conformal_quantile package: probably

Prediction intervals via conformal inference and quantile regression

function: int_conformal_quantile.workflow package: probably

Prediction intervals via split conformal inference

function: int_conformal_split package: probably

Prediction intervals via split conformal inference

function: int_conformal_split.default package: probably

Prediction intervals via split conformal inference

function: int_conformal_split.workflow package: probably

Prediction using orbital objects

function: predict.orbital_class package: orbital

Prediction wrappers for h2o

function: h2o_predict package: agua

Prediction wrappers for h2o

function: h2o_predict_classification package: agua

Prediction wrappers for h2o

function: h2o_predict_regression package: agua

Prediction wrappers for h2o

function: predict._H2OAutoML package: agua

Predictions from a bagged model

function: predict.bagger package: baguette

Predictions on animal species

function: species_probs package: probably

Prepares data and distance matrices for metric calculation

function: prep_data_dist package: tidyclust

Prepares parsed model object

function: as_parsed_model.modeldb_lm package: modeldb

Prepares parsed model object

function: as_parsed_model package: tidypredict

Pretrained Word Embeddings of Tokens

function: step_word_embeddings package: textrecipes

Pretrained Word Embeddings of Tokens

function: tidy.step_word_embeddings package: textrecipes

Print a Recipe

function: print.recipe package: recipes

Print methods

function: print.infer package: infer

Print methods

function: print.infer_layer package: infer

Print methods

function: print.infer_dist package: infer

Print number of predictors and principal components used.

function: print.apd_hat_values package: applicable

Print number of predictors and principal components used.

function: print.apd_pca package: applicable

Print number of predictors and principal components used.

function: print.apd_similarity package: applicable

Probability calibration plots via binning

function: cal_plot_breaks package: probably

Probability calibration plots via binning

function: cal_plot_breaks.data.frame package: probably

Probability calibration plots via binning

function: cal_plot_breaks.tune_results package: probably

Probability calibration plots via binning

function: cal_plot_breaks.grouped_df package: probably

Probability calibration plots via logistic regression

function: cal_plot_logistic package: probably

Probability calibration plots via logistic regression

function: cal_plot_logistic.data.frame package: probably

Probability calibration plots via logistic regression

function: cal_plot_logistic.tune_results package: probably

Probability calibration plots via logistic regression

function: cal_plot_logistic.grouped_df package: probably

Probability calibration plots via moving windows

function: cal_plot_windowed package: probably

Probability calibration plots via moving windows

function: cal_plot_windowed.data.frame package: probably

Probability calibration plots via moving windows

function: cal_plot_windowed.tune_results package: probably

Probability calibration plots via moving windows

function: cal_plot_windowed.grouped_df package: probably

Process a series of workflows

function: workflow_map package: workflowsets

Proportion of Randomly Selected Predictors

function: mtry_prop package: dials

Proportion of data used for validation

function: validation_set_prop package: dials

Proportion of predictors

function: predictor_prop package: dials

Proportion of top predictors

function: prop_terms package: dials

Psuedo-Huber Loss

function: huber_loss_pseudo package: yardstick

Psuedo-Huber Loss

function: huber_loss_pseudo.data.frame package: yardstick

Psuedo-Huber Loss

function: huber_loss_pseudo_vec package: yardstick

Quadratic discriminant analysis

function: discrim_quad package: parsnip

R squared

function: rsq package: yardstick

R squared

function: rsq.data.frame package: yardstick

R squared

function: rsq_vec package: yardstick

R squared - traditional

function: rsq_trad package: yardstick

R squared - traditional

function: rsq_trad.data.frame package: yardstick

R squared - traditional

function: rsq_trad_vec package: yardstick

Radial basis function kernel PCA signal extraction

function: step_kpca_rbf package: recipes

Radial basis function support vector machines

function: svm_rbf package: parsnip

Radius

function: radius package: tidyclust

Random forest

function: rand_forest package: parsnip

Rank score based on dplyr::dense_rank(), where tied values receive the same rank and ranks are without gaps (singular)

function: rank_best_score_dense package: filtro

Rank score based on dplyr::min_rank(), where tied values receive the same rank and ranks are with gaps (singular)

function: rank_best_score_min package: filtro

Rank the results by a metric

function: rank_results package: workflowsets

Ranked probability scores for ordinal classification models

function: ranked_prob_score package: yardstick

Ranked probability scores for ordinal classification models

function: ranked_prob_score.data.frame package: yardstick

Ranked probability scores for ordinal classification models

function: ranked_prob_score_vec package: yardstick

Rates of Cricket Chirps

function: crickets package: modeldata

Ratio of performance to deviation

function: rpd package: yardstick

Ratio of performance to deviation

function: rpd.data.frame package: yardstick

Ratio of performance to deviation

function: rpd_vec package: yardstick

Ratio of performance to inter-quartile

function: rpiq package: yardstick

Ratio of performance to inter-quartile

function: rpiq.data.frame package: yardstick

Ratio of performance to inter-quartile

function: rpiq_vec package: yardstick

Ratio variable creation

function: step_ratio package: recipes

Ratio variable creation

function: denom_vars package: recipes

Raw cover type data

function: covers package: modeldata

Re-arrange a correlation data frame

function: rearrange package: corrr

Read orbital json file

function: orbital_json_read package: orbital

Recall

function: recall package: yardstick

Recall

function: recall.data.frame package: yardstick

Recall

function: recall_vec package: yardstick

Receiver operator curve

function: roc_curve package: yardstick

Receiver operator curve

function: roc_curve.data.frame package: yardstick

Recent Ames Iowa Houses

function: ames_new package: applicable

Refresh a preprocessing blueprint

function: refresh_blueprint package: hardhat

function: set_model_arg package: modelenv

function: get_model_arg package: modelenv

function: set_dependency package: modelenv

function: get_dependency package: modelenv

function: set_encoding package: modelenv

function: get_encoding package: modelenv

function: set_model_engine package: modelenv

function: set_fit package: modelenv

function: get_fit package: modelenv

function: set_model_mode package: modelenv

function: set_new_model package: modelenv

function: set_pred package: modelenv

function: get_pred_type package: modelenv

Regression calibration plots

function: cal_plot_regression package: probably

Regression calibration plots

function: cal_plot_regression.data.frame package: probably

Regression calibration plots

function: cal_plot_regression.tune_results package: probably

Regression calibration plots

function: cal_plot_regression.grouped_df package: probably

Regularized discriminant analysis

function: discrim_regularized package: parsnip

Relabels clusters to match another cluster assignment

function: reconcile_clusterings_mapping package: tidyclust

Relative root mean squared error

function: rmse_relative package: yardstick

Relative root mean squared error

function: rmse_relative.data.frame package: yardstick

Relative root mean squared error

function: rmse_relative_vec package: yardstick

Relevel factors to a desired level

function: step_relevel package: recipes

Remove Points Near Other Classes

function: nearmiss package: themis

Remove Points Near Other Classes

function: step_nearmiss package: themis

Remove Points Near Other Classes

function: tidy.step_nearmiss package: themis

Remove Tomek's links

function: tomek package: themis

Remove Tomek’s Links

function: step_tomek package: themis

Remove Tomek’s Links

function: tidy.step_tomek package: themis

Remove observations with missing values

function: step_naomit package: recipes

Remove some tuning parameter results

function: filter_parameters package: tune

Rename multiple columns using dplyr

function: step_rename_at package: recipes

Rename variables by name using dplyr

function: step_rename package: recipes

Repair a model call object

function: repair_call package: parsnip

Resampled bagged tree results

function: cars_bag_vfld package: shinymodels

Resolve a Model Specification for a Computational Engine

function: translate package: parsnip

Resolve a Model Specification for a Computational Engine

function: translate.default package: parsnip

Resolve a Model Specification for a Computational Engine

function: translate_tidyclust package: tidyclust

Resolve a Model Specification for a Computational Engine

function: translate_tidyclust.default package: tidyclust

Resolve conflicts between tidymodels packages and others

function: tidymodels_prefer package: tidymodels

Retrieve individual rsplits objects from an rset

function: get_rsplit package: rsample

Retrieve individual rsplits objects from an rset

function: get_rsplit.rset package: rsample

Retrieve individual rsplits objects from an rset

function: get_rsplit.default package: rsample

Returns a Tidy Eval formula to calculate fitted values

function: tidypredict_fit package: tidypredict

Returns a Tidy Eval formula to calculate prediction interval.

function: tidypredict_interval package: tidypredict

Returns a correlation table with the selected fields only

function: dice package: corrr

Reverse the analysis and assessment sets

function: reverse_splits package: rsample

Reverse the analysis and assessment sets

function: reverse_splits.default package: rsample

Reverse the analysis and assessment sets

function: reverse_splits.permutations package: rsample

Reverse the analysis and assessment sets

function: reverse_splits.perm_split package: rsample

Reverse the analysis and assessment sets

function: reverse_splits.rsplit package: rsample

Reverse the analysis and assessment sets

function: reverse_splits.rset package: rsample

Role Selection

function: has_role package: recipes

Role Selection

function: has_type package: recipes

Role Selection

function: all_outcomes package: recipes

Role Selection

function: all_predictors package: recipes

Role Selection

function: all_date package: recipes

Role Selection

function: all_date_predictors package: recipes

Role Selection

function: all_datetime package: recipes

Role Selection

function: all_datetime_predictors package: recipes

Role Selection

function: all_double package: recipes

Role Selection

function: all_double_predictors package: recipes

Role Selection

function: all_factor package: recipes

Role Selection

function: all_factor_predictors package: recipes

Role Selection

function: all_integer package: recipes

Role Selection

function: all_integer_predictors package: recipes

Role Selection

function: all_logical package: recipes

Role Selection

function: all_logical_predictors package: recipes

Role Selection

function: all_nominal package: recipes

Role Selection

function: all_nominal_predictors package: recipes

Role Selection

function: all_numeric package: recipes

Role Selection

function: all_numeric_predictors package: recipes

Role Selection

function: all_ordered package: recipes

Role Selection

function: all_ordered_predictors package: recipes

Role Selection

function: all_string package: recipes

Role Selection

function: all_string_predictors package: recipes

Role Selection

function: all_unordered package: recipes

Role Selection

function: all_unordered_predictors package: recipes

Role Selection

function: current_info package: recipes

Role Selection

function: all_tokenized package: textrecipes

Role Selection

function: all_tokenized_predictors package: textrecipes

Rolling Origin Forecast Resampling

function: rolling_origin package: rsample

Rolling summary statistic for moving windows

function: summary_stat package: dials

Rolling summary statistic for moving windows

function: values_summary_stat package: dials

Root mean squared error

function: rmse package: yardstick

Root mean squared error

function: rmse.data.frame package: yardstick

Root mean squared error

function: rmse_vec package: yardstick

Royston-Sauerbei D statistic

function: royston_survival package: yardstick

Royston-Sauerbei D statistic

function: royston_survival.data.frame package: yardstick

Royston-Sauerbei D statistic

function: royston_survival_vec package: yardstick

RuleFit models

function: rule_fit package: parsnip

S3 method to get fitted model summary info depending on engine

function: extract_fit_summary package: tidyclust

S7 subclass of base R's list for method dispatch

function: class_score_list package: filtro

SMOTE Algorithm

function: smote package: themis

SMOTENC Algorithm

function: smotenc package: themis

Sacramento CA home prices

function: Sacramento package: modeldata

Sample rows using dplyr

function: step_sample package: recipes

Sample sentences with emojis

function: emoji_samples package: textrecipes

Sample time series data

function: drinks package: modeldata

Sampling for the Apparent Error Rate

function: apparent package: rsample

Save orbital object as json file

function: orbital_json_write package: orbital

Scaling numeric data

function: step_scale package: recipes

Scaling numeric data to a specific range

function: step_range package: recipes

Score new samples using hat values

function: score.apd_hat_values package: applicable

Score new samples using similarity methods

function: score.apd_similarity package: applicable

Scoring via analysis of variance hypothesis tests

function: score_aov_pval package: filtro

Scoring via analysis of variance hypothesis tests

function: score_aov_fstat package: filtro

Scoring via area under the Receiver Operating Characteristic curve (ROC AUC)

function: score_roc_auc package: filtro

Scoring via correlation coefficient

function: score_cor_pearson package: filtro

Scoring via correlation coefficient

function: score_cor_spearman package: filtro

Scoring via entropy-based filters

function: score_info_gain package: filtro

Scoring via entropy-based filters

function: score_gain_ratio package: filtro

Scoring via entropy-based filters

function: score_sym_uncert package: filtro

Scoring via random forests

function: score_imp_rf package: filtro

Scoring via random forests

function: score_imp_rf_conditional package: filtro

Scoring via random forests

function: score_imp_rf_oblique package: filtro

Scoring via the chi-squared test or Fisher's exact test

function: score_xtab_pval_chisq package: filtro

Scoring via the chi-squared test or Fisher's exact test

function: score_xtab_pval_fisher package: filtro

Scream

function: scream package: hardhat

Select variables using dplyr

function: step_select package: recipes

Sensitivity

function: sens package: yardstick

Sensitivity

function: sens.data.frame package: yardstick

Sensitivity

function: sens_vec package: yardstick

Sensitivity

function: sensitivity package: yardstick

Sensitivity

function: sensitivity.data.frame package: yardstick

Sensitivity

function: sensitivity_vec package: yardstick

Sentencepiece Tokenization of Character Variables

function: step_tokenize_sentencepiece package: textrecipes

Sentencepiece Tokenization of Character Variables

function: tidy.step_tokenize_sentencepiece package: textrecipes

Set categorical feature mappings for CatBoost model

function: set_catboost_categories package: tidypredict

Set up bootstrap replicates of a dplyr operation

function: bootstrap package: broom

Shade histogram area beyond an observed statistic

function: shade_p_value package: infer

Shade histogram area beyond an observed statistic

function: shade_pvalue package: infer

Shave off upper/lower triangle.

function: shave package: corrr

Show best desirability scores, based on number of predictors (plural)

function: show_best_desirability_num package: filtro

Show best desirability scores, based on proportion of predictors (plural)

function: show_best_desirability_prop package: filtro

Show best score, based on based on cutoff value (singular)

function: show_best_score_cutoff package: filtro

Show best score, based on number of predictors (singular)

function: show_best_score_num package: filtro

Show best score, based on number or proportion of predictors with optional cutoff value (singular)

function: show_best_score_dual package: filtro

Show best score, based on proportion of predictors (singular)

function: show_best_score_prop package: filtro

Show token output of recipe

function: show_tokens package: textrecipes

Shuffle variables

function: step_shuffle package: recipes

Simple Training/Test Set Splitting

function: initial_split package: rsample

Simple Training/Test Set Splitting

function: initial_time_split package: rsample

Simple Training/Test Set Splitting

function: training package: rsample

Simple Training/Test Set Splitting

function: training.default package: rsample

Simple Training/Test Set Splitting

function: training.rsplit package: rsample

Simple Training/Test Set Splitting

function: testing package: rsample

Simple Training/Test Set Splitting

function: testing.default package: rsample

Simple Training/Test Set Splitting

function: testing.rsplit package: rsample

Simple Training/Test Set Splitting

function: group_initial_split package: rsample

Simple Transformation Functions

function: no_trans package: tidyposterior

Simple Transformation Functions

function: logit_trans package: tidyposterior

Simple Transformation Functions

function: Fisher_trans package: tidyposterior

Simple Transformation Functions

function: ln_trans package: tidyposterior

Simple Transformation Functions

function: inv_trans package: tidyposterior

Simple kmeans routine that works in-database

function: simple_kmeans_db package: modeldb

Simple value assignments for novel factor levels

function: step_novel package: recipes

Simulate datasets

function: sim_classification package: modeldata

Simulate datasets

function: sim_regression package: modeldata

Simulate datasets

function: sim_noise package: modeldata

Simulate datasets

function: sim_logistic package: modeldata

Simulate datasets

function: sim_multinomial package: modeldata

Simulated longitudinal Poisson counts

function: longitudinal_counts package: multilevelmod

Single layer neural network

function: mlp package: parsnip

Smithsonian museums

function: Smithsonian package: modeldata

Solubility Predictions from MARS Model

function: solubility_test package: yardstick

Solubility predictions from MARS model

function: solubility_test package: modeldata

Sort rows using dplyr

function: step_arrange package: recipes

Space-filling parameter grids

function: grid_space_filling package: dials

Space-filling parameter grids

function: grid_space_filling.default package: dials

Space-filling parameter grids

function: grid_space_filling.parameters package: dials

Space-filling parameter grids

function: grid_space_filling.list package: dials

Space-filling parameter grids

function: grid_space_filling.param package: dials

Sparse Bayesian PCA Signal Extraction

function: step_pca_sparse_bayes package: embed

Sparse Bayesian PCA Signal Extraction

function: tidy.step_pca_sparse_bayes package: embed

Sparse PCA Signal Extraction

function: step_pca_sparse package: embed

Sparse PCA Signal Extraction

function: tidy.step_pca_sparse package: embed

Spatial Clustering Cross-Validation

function: spatial_clustering_cv package: spatialsample

Spatial block cross-validation

function: spatial_block_cv package: spatialsample

Spatial sign preprocessing

function: step_spatialsign package: recipes

Specificity

function: spec package: yardstick

Specificity

function: spec.data.frame package: yardstick

Specificity

function: spec_vec package: yardstick

Specificity

function: specificity package: yardstick

Specificity

function: specificity.data.frame package: yardstick

Specificity

function: specificity_vec package: yardstick

Specify response and explanatory variables

function: specify package: infer

Splice final parameters into objects

function: finalize_model_tidyclust package: tidyclust

Splice final parameters into objects

function: finalize_workflow_tidyclust package: tidyclust

Splice final parameters into objects

function: finalize_model package: tune

Splice final parameters into objects

function: finalize_recipe package: tune

Splice final parameters into objects

function: finalize_workflow package: tune

Splice final parameters into objects

function: finalize_tailor package: tune

Spruce up multi-outcome predictions

function: spruce-multiple package: hardhat

Spruce up multi-outcome predictions

function: spruce_numeric_multiple package: hardhat

Spruce up multi-outcome predictions

function: spruce_class_multiple package: hardhat

Spruce up multi-outcome predictions

function: spruce_prob_multiple package: hardhat

Spruce up predictions

function: spruce package: hardhat

Spruce up predictions

function: spruce_numeric package: hardhat

Spruce up predictions

function: spruce_class package: hardhat

Spruce up predictions

function: spruce_prob package: hardhat

Square root transformation

function: step_sqrt package: recipes

Standardize the outcome

function: standardize package: hardhat

Start an RStudio Addin that can write model specifications

function: parsnip_addin package: parsnip

Stemming of Token Variables

function: step_stem package: textrecipes

Stemming of Token Variables

function: tidy.step_stem package: textrecipes

Stretch correlation data frame into long format.

function: stretch package: corrr

Subset of data from the General Social Survey (GSS).

function: gss package: infer

Subset only required columns

function: shrink package: hardhat

Summarize Posterior Distributions of Model Differences

function: summary.posterior_diff package: tidyposterior

Summarize a recipe

function: summary.recipe package: recipes

Summarize the Posterior Distributions of Model Statistics

function: summary.posterior package: tidyposterior

Summary Statistics for Confusion Matrices

function: summary.conf_mat package: yardstick

Supervised Collapsing of Factor Levels

function: step_collapse_cart package: embed

Supervised Collapsing of Factor Levels

function: tidy.step_collapse_cart package: embed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings

function: step_lencode_bayes package: embed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings

function: tidy.step_lencode_bayes package: embed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings

function: step_lencode_mixed package: embed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings

function: tidy.step_lencode_mixed package: embed

Supervised Factor Conversions into Linear Functions using Likelihood Encodings

function: step_lencode_glm package: embed

Supervised Factor Conversions into Linear Functions using Likelihood Encodings

function: tidy.step_lencode_glm package: embed

Supervised Feature Selection via A Single Filter

function: step_predictor_retain package: important

Supervised Feature Selection via Choosing the Top Predictors

function: step_predictor_best package: important

Supervised Multivariate Feature Selection via Desirability Functions

function: step_predictor_desirability package: important

Supervised and unsupervised uniform manifold approximation and projection (UMAP)

function: step_umap package: embed

Supervised and unsupervised uniform manifold approximation and projection (UMAP)

function: tidy.step_umap package: embed

Support for parallel processing in tune

function: parallelism package: tune

Support vector machine parameters

function: cost package: dials

Support vector machine parameters

function: svm_margin package: dials

Survival Analysis Results

function: lung_surv package: yardstick

Survival Model Link Function

function: survival_link package: dials

Survival Model Link Function

function: values_survival_link package: dials

Symmetric Extremal Dependence Index

function: sedi package: yardstick

Symmetric Extremal Dependence Index

function: sedi.data.frame package: yardstick

Symmetric Extremal Dependence Index

function: sedi_vec package: yardstick

Symmetric mean absolute percentage error

function: smape package: yardstick

Symmetric mean absolute percentage error

function: smape.data.frame package: yardstick

Symmetric mean absolute percentage error

function: smape_vec package: yardstick

Synthetic Dataset With a Circle

function: circle_example package: themis

Tate Gallery modern artwork metadata

function: tate_text package: modeldata

Term Frequency-Inverse Document Frequency of Tokens

function: step_tfidf package: textrecipes

Term Frequency-Inverse Document Frequency of Tokens

function: tidy.step_tfidf package: textrecipes

Term frequency of Tokens

function: step_tf package: textrecipes

Term frequency of Tokens

function: tidy.step_tf package: textrecipes

Term frequency weighting methods

function: weight_scheme package: dials

Term frequency weighting methods

function: values_weight_scheme package: dials

Test if an object inherits from class_pred

function: is_class_pred package: probably

Test set results for logistic regression

function: two_class_final package: shinymodels

Tests base predict function against tidypredict

function: tidypredict_test package: tidypredict

Text hashing parameters

function: num_hash package: dials

Text hashing parameters

function: signed_hash package: dials

The agglomeration Linkage method

function: linkage_method package: tidyclust

The agglomeration Linkage method

function: values_linkage_method package: tidyclust

Tidiers for NULL inputs

function: null_tidiers package: broom

Tidiers for NULL inputs

function: tidy.NULL package: broom

Tidiers for NULL inputs

function: glance.NULL package: broom

Tidiers for NULL inputs

function: augment.NULL package: broom

Tidiers for data.frame objects

function: data.frame_tidiers package: broom

Tidiers for data.frame objects

function: tidy.data.frame package: broom

Tidiers for data.frame objects

function: augment.data.frame package: broom

Tidiers for data.frame objects

function: glance.data.frame package: broom

Tidy Resampling Object

function: tidy.rsplit package: rsample

Tidy Resampling Object

function: tidy.rset package: rsample

Tidy Resampling Object

function: tidy.vfold_cv package: rsample

Tidy Resampling Object

function: tidy.nested_cv package: rsample

Tidy a workflow

function: tidy.workflow package: workflows

Tidy a(n) Arima object

function: tidy.Arima package: broom

Tidy a(n) Arima object

function: Arima_tidiers package: broom

Tidy a(n) Gam object

function: tidy.Gam package: broom

Tidy a(n) Gam object

function: Gam_tidiers package: broom

Tidy a(n) Kendall object

function: tidy.Kendall package: broom

Tidy a(n) Kendall object

function: Kendall_tidiers package: broom

Tidy a(n) Kendall object

function: kendall_tidiers package: broom

Tidy a(n) Mclust object

function: tidy.Mclust package: broom

Tidy a(n) Mclust object

function: mclust_tidiers package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: sp_tidiers package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.SpatialPolygonsDataFrame package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.SpatialPolygons package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.Polygons package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.Polygon package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.SpatialLinesDataFrame package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.Lines package: broom

Tidy a(n) SpatialPolygonsDataFrame object

function: tidy.Line package: broom

Tidy a(n) TukeyHSD object

function: tidy.TukeyHSD package: broom

Tidy a(n) aareg object

function: tidy.aareg package: broom

Tidy a(n) aareg object

function: aareg_tidiers package: broom

Tidy a(n) acf object

function: tidy.acf package: broom

Tidy a(n) anova object

function: tidy.anova package: broom

Tidy a(n) aov object

function: tidy.aov package: broom

Tidy a(n) aovlist object

function: tidy.aovlist package: broom

Tidy a(n) betamfx object

function: tidy.betamfx package: broom

Tidy a(n) betareg object

function: tidy.betareg package: broom

Tidy a(n) betareg object

function: betareg_tidiers package: broom

Tidy a(n) biglm object

function: tidy.biglm package: broom

Tidy a(n) binDesign object

function: tidy.binDesign package: broom

Tidy a(n) binDesign object

function: bindesign_tidiers package: broom

Tidy a(n) binWidth object

function: tidy.binWidth package: broom

Tidy a(n) binWidth object

function: binwidth_tidiers package: broom

Tidy a(n) boot object

function: tidy.boot package: broom

Tidy a(n) boot object

function: boot_tidiers package: broom

Tidy a(n) btergm object

function: tidy.btergm package: broom

Tidy a(n) btergm object

function: btergm_tidiers package: broom

Tidy a(n) cch object

function: tidy.cch package: broom

Tidy a(n) cch object

function: cch_tidiers package: broom

Tidy a(n) cld object

function: tidy.cld package: broom

Tidy a(n) clm object

function: tidy.clm package: broom

Tidy a(n) clm object

function: ordinal_tidiers package: broom

Tidy a(n) clmm object

function: tidy.clmm package: broom

Tidy a(n) cmprsk object

function: tidy.crr package: broom

Tidy a(n) cmprsk object

function: cmprsk_tidiers package: broom

Tidy a(n) coeftest object

function: tidy.coeftest package: broom

Tidy a(n) coeftest object

function: lmtest_tidiers package: broom

Tidy a(n) coeftest object

function: coeftest_tidiers package: broom

Tidy a(n) confint.glht object

function: tidy.confint.glht package: broom

Tidy a(n) confusionMatrix object

function: tidy.confusionMatrix package: broom

Tidy a(n) confusionMatrix object

function: caret_tidiers package: broom

Tidy a(n) confusionMatrix object

function: confusionMatrix_tidiers package: broom

Tidy a(n) coxph object

function: tidy.coxph package: broom

Tidy a(n) coxph object

function: coxph_tidiers package: broom

Tidy a(n) cv.glmnet object

function: tidy.cv.glmnet package: broom

Tidy a(n) drc object

function: tidy.drc package: broom

Tidy a(n) drc object

function: drc_tidiers package: broom

Tidy a(n) emmGrid object

function: tidy.emmGrid package: broom

Tidy a(n) epi.2by2 object

function: tidy.epi.2by2 package: broom

Tidy a(n) epi.2by2 object

function: epiR_tidiers package: broom

Tidy a(n) ergm object

function: tidy.ergm package: broom

Tidy a(n) ergm object

function: ergm_tidiers package: broom

Tidy a(n) factanal object

function: tidy.factanal package: broom

Tidy a(n) factanal object

function: factanal_tidiers package: broom

Tidy a(n) felm object

function: tidy.felm package: broom

Tidy a(n) felm object

function: felm_tidiers package: broom

Tidy a(n) felm object

function: lfe_tidiers package: broom

Tidy a(n) fitdistr object

function: tidy.fitdistr package: broom

Tidy a(n) fitdistr object

function: fitdistr_tidiers package: broom

Tidy a(n) fixest object

function: tidy.fixest package: broom

Tidy a(n) gam object

function: tidy.gam package: broom

Tidy a(n) gam object

function: mgcv_tidiers package: broom

Tidy a(n) gam object

function: gam_tidiers package: broom

Tidy a(n) garch object

function: glance.garch package: broom

Tidy a(n) garch object

function: tidy.garch package: broom

Tidy a(n) garch object

function: garch_tidiers package: broom

Tidy a(n) geeglm object

function: tidy.geeglm package: broom

Tidy a(n) geeglm object

function: geeglm_tidiers package: broom

Tidy a(n) geeglm object

function: geepack_tidiers package: broom

Tidy a(n) glht object

function: tidy.glht package: broom

Tidy a(n) glht object

function: multcomp_tidiers package: broom

Tidy a(n) glm object

function: tidy.glm package: broom

Tidy a(n) glmRob object

function: tidy.glmRob package: broom

Tidy a(n) glmnet object

function: tidy.glmnet package: broom

Tidy a(n) glmnet object

function: glmnet_tidiers package: broom

Tidy a(n) glmrob object

function: tidy.glmrob package: broom

Tidy a(n) gmm object

function: tidy.gmm package: broom

Tidy a(n) gmm object

function: gmm_tidiers package: broom

Tidy a(n) irlba object masquerading as list

function: tidy_irlba package: broom

Tidy a(n) irlba object masquerading as list

function: tidy.irlba package: broom

Tidy a(n) irlba object masquerading as list

function: irlba_tidiers package: broom

Tidy a(n) ivreg object

function: tidy.ivreg package: broom

Tidy a(n) ivreg object

function: ivreg_tidiers package: broom

Tidy a(n) ivreg object

function: aer_tidiers package: broom

Tidy a(n) kappa object

function: tidy.kappa package: broom

Tidy a(n) kappa object

function: kappa_tidiers package: broom

Tidy a(n) kappa object

function: psych_tidiers package: broom

Tidy a(n) kde object

function: tidy.kde package: broom

Tidy a(n) kde object

function: kde_tidiers package: broom

Tidy a(n) kde object

function: ks_tidiers package: broom

Tidy a(n) kmeans object

function: tidy.kmeans package: broom

Tidy a(n) kmeans object

function: kmeans_tidiers package: broom

Tidy a(n) lavaan object

function: tidy.lavaan package: broom

Tidy a(n) lavaan object

function: lavaan_tidiers package: broom

Tidy a(n) lavaan object

function: sem_tidiers package: broom

Tidy a(n) lavaan object

function: cfa_tidiers package: broom

Tidy a(n) lm object

function: tidy.lm package: broom

Tidy a(n) lm object

function: lm_tidiers package: broom

Tidy a(n) lm.beta object

function: tidy.lm.beta package: broom

Tidy a(n) lmRob object

function: tidy.lmRob package: broom

Tidy a(n) lmRob object

function: robust_tidiers package: broom

Tidy a(n) lmodel2 object

function: tidy.lmodel2 package: broom

Tidy a(n) lmodel2 object

function: lmodel2_tidiers package: broom

Tidy a(n) lmrob object

function: tidy.lmrob package: broom

Tidy a(n) lmrob object

function: robustbase_tidiers package: broom

Tidy a(n) loess object

function: augment.loess package: broom

Tidy a(n) loess object

function: loess_tidiers package: broom

Tidy a(n) lsmobj object

function: tidy.lsmobj package: broom

Tidy a(n) lsmobj object

function: emmeans_tidiers package: broom

Tidy a(n) manova object

function: tidy.manova package: broom

Tidy a(n) map object

function: tidy.map package: broom

Tidy a(n) map object

function: maps_tidiers package: broom

Tidy a(n) margins object

function: tidy.margins package: broom

Tidy a(n) margins object

function: margins_tidiers package: broom

Tidy a(n) mediate object

function: tidy.mediate package: broom

Tidy a(n) mediate object

function: mediate_tidiers package: broom

Tidy a(n) mfx object

function: tidy.mfx package: broom

Tidy a(n) mfx object

function: tidy.logitmfx package: broom

Tidy a(n) mfx object

function: tidy.negbinmfx package: broom

Tidy a(n) mfx object

function: tidy.poissonmfx package: broom

Tidy a(n) mfx object

function: tidy.probitmfx package: broom

Tidy a(n) mjoint object

function: tidy.mjoint package: broom

Tidy a(n) mjoint object

function: mjoint_tidiers package: broom

Tidy a(n) mjoint object

function: joinerml_tidiers package: broom

Tidy a(n) mle2 object

function: tidy.mle2 package: broom

Tidy a(n) mle2 object

function: mle2_tidiers package: broom

Tidy a(n) mle2 object

function: bbmle_tidiers package: broom

Tidy a(n) mlm object

function: tidy.mlm package: broom

Tidy a(n) muhaz object

function: tidy.muhaz package: broom

Tidy a(n) muhaz object

function: muhaz_tidiers package: broom

Tidy a(n) negbin object

function: tidy.negbin package: broom

Tidy a(n) nlrq object

function: augment.nlrq package: broom

Tidy a(n) nlrq object

function: tidy.nlrq package: broom

Tidy a(n) nlrq object

function: nlrq_tidiers package: broom

Tidy a(n) nls object

function: tidy.nls package: broom

Tidy a(n) nls object

function: nls_tidiers package: broom

Tidy a(n) optim object masquerading as list

function: glance_optim package: broom

Tidy a(n) optim object masquerading as list

function: glance.optim package: broom

Tidy a(n) optim object masquerading as list

function: tidy_optim package: broom

Tidy a(n) optim object masquerading as list

function: optim_tidiers package: broom

Tidy a(n) optim object masquerading as list

function: tidy.optim package: broom

Tidy a(n) pairwise.htest object

function: tidy.pairwise.htest package: broom

Tidy a(n) pam object

function: tidy.pam package: broom

Tidy a(n) pam object

function: pam_tidiers package: broom

Tidy a(n) plm object

function: tidy.plm package: broom

Tidy a(n) plm object

function: plm_tidiers package: broom

Tidy a(n) poLCA object

function: tidy.poLCA package: broom

Tidy a(n) poLCA object

function: poLCA_tidiers package: broom

Tidy a(n) polr object

function: tidy.polr package: broom

Tidy a(n) polr object

function: polr_tidiers package: broom

Tidy a(n) power.htest object

function: tidy.power.htest package: broom

Tidy a(n) prcomp object

function: tidy.prcomp package: broom

Tidy a(n) prcomp object

function: prcomp_tidiers package: broom

Tidy a(n) pyears object

function: tidy.pyears package: broom

Tidy a(n) pyears object

function: pyears_tidiers package: broom

Tidy a(n) rcorr object

function: tidy.rcorr package: broom

Tidy a(n) rcorr object

function: rcorr_tidiers package: broom

Tidy a(n) rcorr object

function: Hmisc_tidiers package: broom

Tidy a(n) ref.grid object

function: tidy.ref.grid package: broom

Tidy a(n) regsubsets object

function: tidy.regsubsets package: broom

Tidy a(n) regsubsets object

function: leaps_tidiers package: broom

Tidy a(n) ridgelm object

function: tidy.ridgelm package: broom

Tidy a(n) ridgelm object

function: ridgelm_tidiers package: broom

Tidy a(n) rlm object

function: tidy.rlm package: broom

Tidy a(n) rma object

function: tidy.rma package: broom

Tidy a(n) roc object

function: tidy.roc package: broom

Tidy a(n) roc object

function: auc_tidiers package: broom

Tidy a(n) roc object

function: roc_tidiers package: broom

Tidy a(n) rq object

function: tidy.rq package: broom

Tidy a(n) rq object

function: rq_tidiers package: broom

Tidy a(n) rq object

function: quantreg_tidiers package: broom

Tidy a(n) rqs object

function: tidy.rqs package: broom

Tidy a(n) rqs object

function: rqs_tidiers package: broom

Tidy a(n) smooth.spine object

function: glance.smooth.spline package: broom

Tidy a(n) smooth.spline object

function: augment.smooth.spline package: broom

Tidy a(n) smooth.spline object

function: smooth.spline_tidiers package: broom

Tidy a(n) spec object

function: tidy.spec package: broom

Tidy a(n) speedglm object

function: tidy.speedglm package: broom

Tidy a(n) speedglm object

function: speedglm_tidiers package: broom

Tidy a(n) speedlm object

function: tidy.speedlm package: broom

Tidy a(n) speedlm object

function: speedlm_tidiers package: broom

Tidy a(n) summary.glht object

function: tidy.summary.glht package: broom

Tidy a(n) summary.lm object

function: tidy.summary.lm package: broom

Tidy a(n) summary_emm object

function: tidy.summary_emm package: broom

Tidy a(n) survdiff object

function: tidy.survdiff package: broom

Tidy a(n) survdiff object

function: survdiff_tidiers package: broom

Tidy a(n) survexp object

function: tidy.survexp package: broom

Tidy a(n) survexp object

function: sexpfit_tidiers package: broom

Tidy a(n) survexp object

function: survexp_tidiers package: broom

Tidy a(n) survfit object

function: tidy.survfit package: broom

Tidy a(n) survfit object

function: survfit_tidiers package: broom

Tidy a(n) survreg object

function: tidy.survreg package: broom

Tidy a(n) survreg object

function: survreg_tidiers package: broom

Tidy a(n) svd object masquerading as list

function: tidy_svd package: broom

Tidy a(n) svd object masquerading as list

function: svd_tidiers package: broom

Tidy a(n) svyglm object

function: tidy.svyglm package: broom

Tidy a(n) svyolr object

function: tidy.svyolr package: broom

Tidy a(n) svyolr object

function: svyolr_tidiers package: broom

Tidy a(n) systemfit object

function: tidy.systemfit package: broom

Tidy a(n) systemfit object

function: systemfit_tidiers package: broom

Tidy a(n) table object

function: tidy.table package: broom

Tidy a(n) ts object

function: tidy.ts package: broom

Tidy a(n) varest object

function: tidy.varest package: broom

Tidy a(n) varest object

function: vars_tidiers package: broom

Tidy a(n) xyz object masquerading as list

function: tidy_xyz package: broom

Tidy a(n) xyz object masquerading as list

function: xyz_tidiers package: broom

Tidy a(n) zoo object

function: tidy.zoo package: broom

Tidy a(n) zoo object

function: zoo_tidiers package: broom

Tidy atomic vectors

function: tidy.numeric package: broom

Tidy atomic vectors

function: tidy.character package: broom

Tidy atomic vectors

function: tidy.logical package: broom

Tidy chi-squared test

function: chisq_test package: infer

Tidy chi-squared test statistic

function: chisq_stat package: infer

Tidy methods for pls and spls objects

function: tidy.mixo_pls package: plsmod

Tidy methods for pls and spls objects

function: tidy.mixo_spls package: plsmod

Tidy proportion test

function: prop_test package: infer

Tidy t-test

function: t_test package: infer

Tidy t-test statistic

function: t_stat package: infer

Tidy the parsed model results

function: tidy.pm_regression package: tidypredict

Tidy the result of a recipe

function: tidy.step_BoxCox package: recipes

Tidy the result of a recipe

function: tidy.step_YeoJohnson package: recipes

Tidy the result of a recipe

function: tidy.step_arrange package: recipes

Tidy the result of a recipe

function: tidy.step_bin2factor package: recipes

Tidy the result of a recipe

function: tidy.step_bs package: recipes

Tidy the result of a recipe

function: tidy.step_center package: recipes

Tidy the result of a recipe

function: tidy.check_class package: recipes

Tidy the result of a recipe

function: tidy.step_classdist package: recipes

Tidy the result of a recipe

function: tidy.step_classdist_shrunken package: recipes

Tidy the result of a recipe

function: tidy.check_cols package: recipes

Tidy the result of a recipe

function: tidy.step_corr package: recipes

Tidy the result of a recipe

function: tidy.step_count package: recipes

Tidy the result of a recipe

function: tidy.step_cut package: recipes

Tidy the result of a recipe

function: tidy.step_date package: recipes

Tidy the result of a recipe

function: tidy.step_depth package: recipes

Tidy the result of a recipe

function: tidy.step_discretize package: recipes

Tidy the result of a recipe

function: tidy.step_dummy package: recipes

Tidy the result of a recipe

function: tidy.step_dummy_extract package: recipes

Tidy the result of a recipe

function: tidy.step_dummy_multi_choice package: recipes

Tidy the result of a recipe

function: tidy.step_factor2string package: recipes

Tidy the result of a recipe

function: tidy.step_filter package: recipes

Tidy the result of a recipe

function: tidy.step_filter_missing package: recipes

Tidy the result of a recipe

function: tidy.step_geodist package: recipes

Tidy the result of a recipe

function: tidy.step_harmonic package: recipes

Tidy the result of a recipe

function: tidy.step_holiday package: recipes

Tidy the result of a recipe

function: tidy.step_hyperbolic package: recipes

Tidy the result of a recipe

function: tidy.step_ica package: recipes

Tidy the result of a recipe

function: tidy.step_impute_bag package: recipes

Tidy the result of a recipe

function: tidy.step_impute_knn package: recipes

Tidy the result of a recipe

function: tidy.step_impute_linear package: recipes

Tidy the result of a recipe

function: tidy.step_impute_lower package: recipes

Tidy the result of a recipe

function: tidy.step_impute_mean package: recipes

Tidy the result of a recipe

function: tidy.step_impute_median package: recipes

Tidy the result of a recipe

function: tidy.step_impute_mode package: recipes

Tidy the result of a recipe

function: tidy.step_impute_roll package: recipes

Tidy the result of a recipe

function: tidy.step_indicate_na package: recipes

Tidy the result of a recipe

function: tidy.step_integer package: recipes

Tidy the result of a recipe

function: tidy.step_interact package: recipes

Tidy the result of a recipe

function: tidy.step_intercept package: recipes

Tidy the result of a recipe

function: tidy.step_inverse package: recipes

Tidy the result of a recipe

function: tidy.step_invlogit package: recipes

Tidy the result of a recipe

function: tidy.step_isomap package: recipes

Tidy the result of a recipe

function: tidy.step_kpca package: recipes

Tidy the result of a recipe

function: tidy.step_kpca_poly package: recipes

Tidy the result of a recipe

function: tidy.step_kpca_rbf package: recipes

Tidy the result of a recipe

function: tidy.step_lag package: recipes

Tidy the result of a recipe

function: tidy.step_lincomb package: recipes

Tidy the result of a recipe

function: tidy.step_log package: recipes

Tidy the result of a recipe

function: tidy.step_logit package: recipes

Tidy the result of a recipe

function: tidy.check_missing package: recipes

Tidy the result of a recipe

function: tidy.step_mutate package: recipes

Tidy the result of a recipe

function: tidy.step_mutate_at package: recipes

Tidy the result of a recipe

function: tidy.step_naomit package: recipes

Tidy the result of a recipe

function: tidy.check_new_values package: recipes

Tidy the result of a recipe

function: tidy.step_nnmf package: recipes

Tidy the result of a recipe

function: tidy.step_nnmf_sparse package: recipes

Tidy the result of a recipe

function: tidy.step_normalize package: recipes

Tidy the result of a recipe

function: tidy.step_novel package: recipes

Tidy the result of a recipe

function: tidy.step_ns package: recipes

Tidy the result of a recipe

function: tidy.step_num2factor package: recipes

Tidy the result of a recipe

function: tidy.step_nzv package: recipes

Tidy the result of a recipe

function: tidy.step_ordinalscore package: recipes

Tidy the result of a recipe

function: tidy.step_other package: recipes

Tidy the result of a recipe

function: tidy.step_pca package: recipes

Tidy the result of a recipe

function: tidy.step_percentile package: recipes

Tidy the result of a recipe

function: tidy.step_pls package: recipes

Tidy the result of a recipe

function: tidy.step_poly package: recipes

Tidy the result of a recipe

function: tidy.step_poly_bernstein package: recipes

Tidy the result of a recipe

function: tidy.step_profile package: recipes

Tidy the result of a recipe

function: tidy.step_range package: recipes

Tidy the result of a recipe

function: tidy.check_range package: recipes

Tidy the result of a recipe

function: tidy.step_ratio package: recipes

Tidy the result of a recipe

function: tidy.step_regex package: recipes

Tidy the result of a recipe

function: tidy.step_relevel package: recipes

Tidy the result of a recipe

function: tidy.step_relu package: recipes

Tidy the result of a recipe

function: tidy.step_rename package: recipes

Tidy the result of a recipe

function: tidy.step_rename_at package: recipes

Tidy the result of a recipe

function: tidy.step_rm package: recipes

Tidy the result of a recipe

function: tidy.step_sample package: recipes

Tidy the result of a recipe

function: tidy.step_scale package: recipes

Tidy the result of a recipe

function: tidy.step_select package: recipes

Tidy the result of a recipe

function: tidy.step_shuffle package: recipes

Tidy the result of a recipe

function: tidy.step_slice package: recipes

Tidy the result of a recipe

function: tidy.step_spatialsign package: recipes

Tidy the result of a recipe

function: tidy.step_spline_b package: recipes

Tidy the result of a recipe

function: tidy.step_spline_convex package: recipes

Tidy the result of a recipe

function: tidy.step_spline_monotone package: recipes

Tidy the result of a recipe

function: tidy.step_spline_natural package: recipes

Tidy the result of a recipe

function: tidy.step_spline_nonnegative package: recipes

Tidy the result of a recipe

function: tidy.step_sqrt package: recipes

Tidy the result of a recipe

function: tidy.step_string2factor package: recipes

Tidy the result of a recipe

function: tidy.recipe package: recipes

Tidy the result of a recipe

function: tidy.step package: recipes

Tidy the result of a recipe

function: tidy.check package: recipes

Tidy the result of a recipe

function: tidy.step_time package: recipes

Tidy the result of a recipe

function: tidy.step_unknown package: recipes

Tidy the result of a recipe

function: tidy.step_unorder package: recipes

Tidy the result of a recipe

function: tidy.step_window package: recipes

Tidy the result of a recipe

function: tidy.step_zv package: recipes

Tidy/glance a(n) durbinWatsonTest object

function: durbinWatsonTest_tidiers package: broom

Tidy/glance a(n) durbinWatsonTest object

function: tidy.durbinWatsonTest package: broom

Tidy/glance a(n) durbinWatsonTest object

function: glance.durbinWatsonTest package: broom

Tidy/glance a(n) htest object

function: tidy.htest package: broom

Tidy/glance a(n) htest object

function: htest_tidiers package: broom

Tidy/glance a(n) htest object

function: glance.htest package: broom

Tidy/glance a(n) leveneTest object

function: leveneTest_tidiers package: broom

Tidy/glance a(n) leveneTest object

function: tidy.leveneTest package: broom

Tidying methods for lists / returned values that are not S3 objects

function: list_tidiers package: broom

Tidying methods for lists / returned values that are not S3 objects

function: tidy.list package: broom

Tidying methods for lists / returned values that are not S3 objects

function: glance.list package: broom

Tidying methods for logit models

function: tidy.mlogit package: broom

Tidying methods for logit models

function: mlogit_tidiers package: broom

Tidying methods for multinomial logistic regression models

function: tidy.multinom package: broom

Tidying methods for multinomial logistic regression models

function: multinom_tidiers package: broom

Tidying methods for multinomial logistic regression models

function: nnet_tidiers package: broom

Tidying methods for spatially autoregressive models

function: tidy.sarlm package: broom

Tidying methods for spatially autoregressive models

function: spatialreg_tidiers package: broom

Time feature generator

function: step_time package: recipes

Time-Dependent Brier score for right censored data

function: brier_survival package: yardstick

Time-Dependent Brier score for right censored data

function: brier_survival.data.frame package: yardstick

Time-Dependent Brier score for right censored data

function: brier_survival_vec package: yardstick

Time-Dependent ROC AUC for Censored Data

function: roc_auc_survival package: yardstick

Time-Dependent ROC AUC for Censored Data

function: roc_auc_survival.data.frame package: yardstick

Time-Dependent ROC AUC for Censored Data

function: roc_auc_survival_vec package: yardstick

Time-Dependent ROC surve for Censored Data

function: roc_curve_survival package: yardstick

Time-Dependent ROC surve for Censored Data

function: roc_curve_survival.data.frame package: yardstick

Time-based Resampling

function: slide-resampling package: rsample

Time-based Resampling

function: sliding_window package: rsample

Time-based Resampling

function: sliding_index package: rsample

Time-based Resampling

function: sliding_period package: rsample

Token types

function: token package: dials

Token types

function: values_token package: dials

Tokenization of Character Variables

function: step_tokenize package: textrecipes

Tokenization of Character Variables

function: tidy.step_tokenize package: textrecipes

Tools for creating new parameter objects

function: new-param package: dials

Tools for creating new parameter objects

function: new_quant_param package: dials

Tools for creating new parameter objects

function: new_qual_param package: dials

Tools for working with H2O AutoML results

function: rank_results.workflow package: agua

Tools for working with H2O AutoML results

function: rank_results._H2OAutoML package: agua

Tools for working with H2O AutoML results

function: rank_results.H2OAutoML package: agua

Tools for working with H2O AutoML results

function: collect_metrics.workflow package: agua

Tools for working with H2O AutoML results

function: collect_metrics._H2OAutoML package: agua

Tools for working with H2O AutoML results

function: collect_metrics.H2OAutoML package: agua

Tools for working with H2O AutoML results

function: tidy._H2OAutoML package: agua

Tools for working with H2O AutoML results

function: get_leaderboard package: agua

Tools for working with H2O AutoML results

function: member_weights package: agua

Tools for working with H2O AutoML results

function: extract_fit_parsnip._H2OAutoML package: agua

Tools for working with H2O AutoML results

function: extract_fit_engine._H2OAutoML package: agua

Tools for working with H2O AutoML results

function: refit.workflow package: agua

Tools for working with H2O AutoML results

function: refit._H2OAutoML package: agua

Tools for working with parameter ranges

function: range_validate package: dials

Tools for working with parameter ranges

function: range_get package: dials

Tools for working with parameter ranges

function: range_set package: dials

Tools for working with parameter values

function: value_validate package: dials

Tools for working with parameter values

function: value_seq package: dials

Tools for working with parameter values

function: value_sample package: dials

Tools for working with parameter values

function: value_transform package: dials

Tools for working with parameter values

function: value_inverse package: dials

Tools for working with parameter values

function: value_set package: dials

Tree frog embryo hatching data

function: tree_frogs package: stacks

Truncate a numeric prediction column

function: bound_prediction package: probably

Truncated PCA Signal Extraction

function: step_pca_truncated package: embed

Truncated PCA Signal Extraction

function: tidy.step_pca_truncated package: embed

Tuned flexible discriminant analysis results

function: scat_fda_bt package: shinymodels

Turn C5.0 and rule-based models into tidy tibbles

function: tidy.C5.0 package: rules

Turn C5.0 and rule-based models into tidy tibbles

function: tidy.cubist package: rules

Turn C5.0 and rule-based models into tidy tibbles

function: tidy.xrf package: rules

Turn a parsnip model object into a tidy tibble

function: tidy.model_fit package: parsnip

Turn a path object into a combined expression

function: path_formulas package: tidypredict

Turn a path object into an expression

function: path_formula package: tidypredict

Turn a tidyclust model object into a tidy tibble

function: tidy.cluster_fit package: tidyclust

Turn orbital object into a R function

function: orbital_r_fun package: orbital

Turn tidymodels objects into orbital objects

function: orbital package: orbital

Turn zero-inflated model results into a tidy tibble

function: tidy_zip package: poissonreg

Turn zero-inflated model results into a tidy tibble

function: tidy.zeroinfl package: poissonreg

Turn zero-inflated model results into a tidy tibble

function: tidy.hurdle package: poissonreg

Tweak a metric function

function: metric_tweak package: yardstick

Two Class Example Data

function: two_class_set package: workflowsets

Two Class Example Data

function: two_class_res package: workflowsets

Two Class Predictions

function: two_class_example package: yardstick

Two class data

function: two_class_dat package: modeldata

Two class predictions

function: two_class_example package: modeldata

Untokenization of Token Variables

function: step_untokenize package: textrecipes

Untokenization of Token Variables

function: tidy.step_untokenize package: textrecipes

Up-Sample a Data Set Based on a Factor Variable

function: step_upsample package: themis

Up-Sample a Data Set Based on a Factor Variable

function: tidy.step_upsample package: themis

Update a cluster specification

function: update.db_clust package: tidyclust

Update a cluster specification

function: update.gm_clust package: tidyclust

Update a cluster specification

function: update.hier_clust package: tidyclust

Update a cluster specification

function: update.k_means package: tidyclust

Update a cluster specification

function: update.mean_shift package: tidyclust

Update a cluster specification

function: tidyclust_update package: tidyclust

Update a preprocessing blueprint

function: update_blueprint package: hardhat

Update a recipe step

function: update.step package: recipes

Update a single parameter in a parameter set

function: update.parameters package: dials

Update components of a workflow within a workflow set

function: update_workflow_model package: workflowsets

Update components of a workflow within a workflow set

function: update_workflow_recipe package: workflowsets

Update role specific requirements

function: update_role_requirements package: recipes

Update tidymodels packages

function: tidymodels_update package: tidymodels

Updating a model specification

function: update.bag_mars package: parsnip

Updating a model specification

function: update.bag_mlp package: parsnip

Updating a model specification

function: update.bag_tree package: parsnip

Updating a model specification

function: update.bart package: parsnip

Updating a model specification

function: update.boost_tree package: parsnip

Updating a model specification

function: update.C5_rules package: parsnip

Updating a model specification

function: update.cubist_rules package: parsnip

Updating a model specification

function: update.decision_tree package: parsnip

Updating a model specification

function: update.discrim_flexible package: parsnip

Updating a model specification

function: update.discrim_linear package: parsnip

Updating a model specification

function: update.discrim_quad package: parsnip

Updating a model specification

function: update.discrim_regularized package: parsnip

Updating a model specification

function: update.gen_additive_mod package: parsnip

Updating a model specification

function: update.linear_reg package: parsnip

Updating a model specification

function: update.logistic_reg package: parsnip

Updating a model specification

function: update.mars package: parsnip

Updating a model specification

function: update.mlp package: parsnip

Updating a model specification

function: update.multinom_reg package: parsnip

Updating a model specification

function: update.naive_Bayes package: parsnip

Updating a model specification

function: update.nearest_neighbor package: parsnip

Updating a model specification

function: update.ordinal_reg package: parsnip

Updating a model specification

function: update.pls package: parsnip

Updating a model specification

function: update.poisson_reg package: parsnip

Updating a model specification

function: update.proportional_hazards package: parsnip

Updating a model specification

function: update.rand_forest package: parsnip

Updating a model specification

function: update.rule_fit package: parsnip

Updating a model specification

function: update.surv_reg package: parsnip

Updating a model specification

function: update.survival_reg package: parsnip

Updating a model specification

function: update.svm_linear package: parsnip

Updating a model specification

function: update.svm_poly package: parsnip

Updating a model specification

function: update.svm_rbf package: parsnip

Updating a model specification

function: parsnip_update package: parsnip

Use same scale for plots of observed vs predicted values

function: coord_obs_pred package: tune

Uses a Beta calibration model to calculate new probabilities

function: cal_estimate_beta package: probably

Uses a Beta calibration model to calculate new probabilities

function: cal_estimate_beta.data.frame package: probably

Uses a Beta calibration model to calculate new probabilities

function: cal_estimate_beta.tune_results package: probably

Uses a Beta calibration model to calculate new probabilities

function: cal_estimate_beta.grouped_df package: probably

Uses a Multinomial calibration model to calculate new probabilities

function: cal_estimate_multinomial package: probably

Uses a Multinomial calibration model to calculate new probabilities

function: cal_estimate_multinomial.data.frame package: probably

Uses a Multinomial calibration model to calculate new probabilities

function: cal_estimate_multinomial.tune_results package: probably

Uses a Multinomial calibration model to calculate new probabilities

function: cal_estimate_multinomial.grouped_df package: probably

Uses a bootstrapped Isotonic regression model to calibrate probabilities

function: cal_estimate_isotonic_boot package: probably

Uses a bootstrapped Isotonic regression model to calibrate probabilities

function: cal_estimate_isotonic_boot.data.frame package: probably

Uses a bootstrapped Isotonic regression model to calibrate probabilities

function: cal_estimate_isotonic_boot.tune_results package: probably

Uses a bootstrapped Isotonic regression model to calibrate probabilities

function: cal_estimate_isotonic_boot.grouped_df package: probably

Uses a linear regression model to calibrate numeric predictions

function: cal_estimate_linear package: probably

Uses a linear regression model to calibrate numeric predictions

function: cal_estimate_linear.data.frame package: probably

Uses a linear regression model to calibrate numeric predictions

function: cal_estimate_linear.tune_results package: probably

Uses a linear regression model to calibrate numeric predictions

function: cal_estimate_linear.grouped_df package: probably

Uses a logistic regression model to calibrate probabilities

function: cal_estimate_logistic package: probably

Uses a logistic regression model to calibrate probabilities

function: cal_estimate_logistic.data.frame package: probably

Uses a logistic regression model to calibrate probabilities

function: cal_estimate_logistic.tune_results package: probably

Uses a logistic regression model to calibrate probabilities

function: cal_estimate_logistic.grouped_df package: probably

Uses an Isotonic regression model to calibrate model predictions.

function: cal_estimate_isotonic package: probably

Uses an Isotonic regression model to calibrate model predictions.

function: cal_estimate_isotonic.data.frame package: probably

Uses an Isotonic regression model to calibrate model predictions.

function: cal_estimate_isotonic.tune_results package: probably

Uses an Isotonic regression model to calibrate model predictions.

function: cal_estimate_isotonic.grouped_df package: probably

Using case weights with parsnip

function: case_weights package: parsnip

Using case weights with recipes

function: case_weights package: recipes

Using sparse data with parsnip

function: sparse_data package: parsnip

Using sparse data with recipes

function: sparse_data package: recipes

Utility functions for interacting with the h2o server

function: h2o_start package: agua

Utility functions for interacting with the h2o server

function: h2o_end package: agua

Utility functions for interacting with the h2o server

function: h2o_running package: agua

Utility functions for interacting with the h2o server

function: h2o_remove package: agua

Utility functions for interacting with the h2o server

function: h2o_remove_all package: agua

Utility functions for interacting with the h2o server

function: h2o_get_model package: agua

Utility functions for interacting with the h2o server

function: h2o_get_frame package: agua

Utility functions for interacting with the h2o server

function: h2o_xgboost_available package: agua

V-Fold Cross-Validation

function: vfold_cv package: rsample

V-Fold Cross-Validation with Buffering

function: spatial_buffer_vfold_cv package: spatialsample

V-Fold Cross-Validation with Buffering

function: spatial_leave_location_out_cv package: spatialsample

Visualize a KMeans Cluster with lots of data

function: plot_kmeans package: modeldb

Visualize a KMeans Cluster with lots of data

function: db_calculate_squares package: modeldb

Visualize importance scores

function: autoplot.importance_perm package: important

Visualize statistical inference

function: visualize package: infer

Visualize statistical inference

function: visualise package: infer

Visualize the Posterior Distributions of Model Differences

function: autoplot.posterior_diff package: tidyposterior

Visualize the Posterior Distributions of Model Statistics

function: autoplot.posterior package: tidyposterior

Visualize the Posterior Distributions of Model Statistics

function: autoplot.perf_mod package: tidyposterior

Visualize the Posterior Distributions of Model Statistics

function: autoplot.perf_mod_workflow_set package: tidyposterior

Watson churn data

function: wa_churn package: modeldata

Weigh the object.

function: weigh package: butcher

Weight of evidence dictionary

function: dictionary package: embed

Weight of evidence transformation

function: step_woe package: embed

Weight of evidence transformation

function: tidy.step_woe package: embed

Weighted table

function: weighted_table package: hardhat

Word frequencies for removal

function: max_times package: dials

Word frequencies for removal

function: min_times package: dials

Wordpiece Tokenization of Character Variables

function: step_tokenize_wordpiece package: textrecipes

Wordpiece Tokenization of Character Variables

function: tidy.step_tokenize_wordpiece package: textrecipes

Working with the modelenv model environment

function: get_model_env package: modelenv

Working with the modelenv model environment

function: get_from_env package: modelenv

Working with the modelenv model environment

function: set_env_val package: modelenv

Wrapper function for preparing recipes within resampling

function: prepper package: recipes

Write a message that respects the line width

function: message_wrap package: tune

Yeo-Johnson transformation

function: step_YeoJohnson package: recipes

Zero variance filter

function: step_zv package: recipes

bonsai: Model Wrappers for Tree-Based Models

function: bonsai-package package: bonsai

bonsai: Model Wrappers for Tree-Based Models

function: bonsai package: bonsai

borderline-SMOTE Algorithm

function: bsmote package: themis

censored: parsnip Engines for Survival Models

function: censored package: censored

censored: parsnip Engines for Survival Models

function: censored-package package: censored

collapse factor levels using stringdist

function: step_collapse_stringdist package: embed

collapse factor levels using stringdist

function: tidy.step_collapse_stringdist package: embed

forge() according to a blueprint

function: run-forge package: hardhat

forge() according to a blueprint

function: run_forge package: hardhat

forge() according to a blueprint

function: run_forge.default_formula_blueprint package: hardhat

forge() according to a blueprint

function: run_forge.default_recipe_blueprint package: hardhat

forge() according to a blueprint

function: run_forge.default_xy_blueprint package: hardhat

infer: a grammar for statistical inference

function: infer-package package: infer

infer: a grammar for statistical inference

function: infer package: infer

mold() according to a blueprint

function: run-mold package: hardhat

mold() according to a blueprint

function: run_mold package: hardhat

mold() according to a blueprint

function: run_mold.default_formula_blueprint package: hardhat

mold() according to a blueprint

function: run_mold.default_recipe_blueprint package: hardhat

mold() according to a blueprint

function: run_mold.default_xy_blueprint package: hardhat

multi_predict() methods for rule-based models

function: multi_predict._cubist package: rules

multi_predict() methods for rule-based models

function: multi_predict._xrf package: rules

stacks: Tidy Model Stacking

function: stacks_description package: stacks

stacks: Tidy Model Stacking

function: stacks_package package: stacks

stacks: Tidy Model Stacking

function: stacks-package package: stacks