Package index
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auto_ml()
- Automatic Machine Learning
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bag_mars()
- Ensembles of MARS models
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bag_mlp()
- Ensembles of neural networks
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bag_tree()
- Ensembles of decision trees
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bart()
- Bayesian additive regression trees (BART)
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boost_tree()
- Boosted trees
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cubist_rules()
- Cubist rule-based regression models
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C5_rules()
- C5.0 rule-based classification models
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decision_tree()
- Decision trees
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discrim_flexible()
- Flexible discriminant analysis
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discrim_linear()
- Linear discriminant analysis
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discrim_quad()
- Quadratic discriminant analysis
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discrim_regularized()
- Regularized discriminant analysis
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gen_additive_mod()
- Generalized additive models (GAMs)
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glm_grouped()
- Fit a grouped binomial outcome from a data set with case weights
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linear_reg()
- Linear regression
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logistic_reg()
- Logistic regression
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mars()
- Multivariate adaptive regression splines (MARS)
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mlp()
- Single layer neural network
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multinom_reg()
- Multinomial regression
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naive_Bayes()
- Naive Bayes models
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nearest_neighbor()
- K-nearest neighbors
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null_model()
- Null model
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pls()
- Partial least squares (PLS)
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poisson_reg()
- Poisson regression models
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proportional_hazards()
- Proportional hazards regression
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rand_forest()
- Random forest
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rule_fit()
- RuleFit models
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survival_reg()
- Parametric survival regression
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svm_linear()
- Linear support vector machines
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svm_poly()
- Polynomial support vector machines
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svm_rbf()
- Radial basis function support vector machines
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autoplot(<model_fit>)
autoplot(<glmnet>)
- Create a ggplot for a model object
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add_rowindex()
- Add a column of row numbers to a data frame
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augment(<model_fit>)
- Augment data with predictions
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case_weights
- Using case weights with parsnip
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case_weights_allowed()
- Determine if case weights are used
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.cols()
.preds()
.obs()
.lvls()
.facts()
.x()
.y()
.dat()
- Data Set Characteristics Available when Fitting Models
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extract_spec_parsnip(<model_fit>)
extract_fit_engine(<model_fit>)
extract_parameter_set_dials(<model_spec>)
extract_parameter_dials(<model_spec>)
extract_fit_time(<model_fit>)
- Extract elements of a parsnip model object
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fit(<model_spec>)
fit_xy(<model_spec>)
- Fit a Model Specification to a Dataset
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reexports
autoplot
%>%
fit
fit_xy
tidy
glance
augment
required_pkgs
extract_spec_parsnip
extract_fit_engine
extract_parameter_set_dials
extract_parameter_dials
tune
frequency_weights
importance_weights
extract_fit_time
varying_args
- Objects exported from other packages
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control_parsnip()
- Control the fit function
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glance(<model_fit>)
- Construct a single row summary "glance" of a model, fit, or other object
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matrix_to_quantile_pred()
- Reformat quantile predictions
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model_fit
- Model Fit Objects
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model_formula
- Formulas with special terms in tidymodels
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model_spec
- Model Specifications
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multi_predict()
- Model predictions across many sub-models
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parsnip_addin()
- Start an RStudio Addin that can write model specifications
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predict(<model_fit>)
predict_raw()
- Model predictions
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repair_call()
- Repair a model call object
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set_args()
set_mode()
- Change elements of a model specification
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set_engine()
- Declare a computational engine and specific arguments
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show_engines()
- Display currently available engines for a model
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sparse_data
- Using sparse data with parsnip
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tidy(<model_fit>)
- Turn a parsnip model object into a tidy tibble
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translate()
- Resolve a Model Specification for a Computational Engine
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update(<bag_mars>)
update(<bag_mlp>)
update(<bag_tree>)
update(<bart>)
update(<boost_tree>)
update(<C5_rules>)
update(<cubist_rules>)
update(<decision_tree>)
update(<discrim_flexible>)
update(<discrim_linear>)
update(<discrim_quad>)
update(<discrim_regularized>)
update(<gen_additive_mod>)
update(<linear_reg>)
update(<logistic_reg>)
update(<mars>)
update(<mlp>)
update(<multinom_reg>)
update(<naive_Bayes>)
update(<nearest_neighbor>)
update(<pls>)
update(<poisson_reg>)
update(<proportional_hazards>)
update(<rand_forest>)
update(<rule_fit>)
update(<surv_reg>)
update(<survival_reg>)
update(<svm_linear>)
update(<svm_poly>)
update(<svm_rbf>)
- Updating a model specification
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ctree_train()
cforest_train()
- A wrapper function for conditional inference tree models
-
condense_control()
- Condense control object into strictly smaller control object
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contr_one_hot()
- Contrast function for one-hot encodings
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set_new_model()
set_model_mode()
set_model_engine()
set_model_arg()
set_dependency()
get_dependency()
set_fit()
get_fit()
set_pred()
get_pred_type()
show_model_info()
pred_value_template()
set_encoding()
get_encoding()
- Tools to Register Models
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maybe_matrix()
maybe_data_frame()
- Fuzzy conversions
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min_cols()
min_rows()
- Execution-time data dimension checks
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max_mtry_formula()
- Determine largest value of mtry from formula. This function potentially caps the value of
mtry
based on a formula and data set. This is a safe approach for survival and/or multivariate models.
-
reexports
autoplot
%>%
fit
fit_xy
tidy
glance
augment
required_pkgs
extract_spec_parsnip
extract_fit_engine
extract_parameter_set_dials
extract_parameter_dials
tune
frequency_weights
importance_weights
extract_fit_time
varying_args
- Objects exported from other packages
-
required_pkgs(<model_spec>)
required_pkgs(<model_fit>)
- Determine required packages for a model
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req_pkgs()
deprecated - Determine required packages for a model
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.extract_surv_status
- Extract survival status
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.extract_surv_time
- Extract survival time
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.model_param_name_key()
- Translate names of model tuning parameters