Man pages for parsnip
A Common API to Modeling and Analysis Functions

add_on_exportsFunctions required for parsnip-adjacent packages
add_rowindexAdd a column of row numbers to a data frame
augmentAugment data with predictions
auto_mlAutomatic Machine Learning
autoplot.model_fitCreate a ggplot for a model object
bag_marsEnsembles of MARS models
bag_mlpEnsembles of neural networks
bag_treeEnsembles of decision trees
bartBayesian additive regression trees (BART)
bart-internalDeveloper functions for predictions via BART models
boost_treeBoosted trees
C5.0_trainBoosted trees via C5.0
C5_rulesC5.0 rule-based classification models
case_weightsUsing case weights with parsnip
check_empty_ellipseCheck to ensure that ellipses are empty
condense_controlCondense control object into strictly smaller control object
control_parsnipControl the fit function
contr_one_hotContrast function for one-hot encodings
convert_helpersHelper functions to convert between formula and matrix...
convert_stan_intervalConvenience function for intervals
ctree_trainA wrapper function for conditional inference tree models
cubist_rulesCubist rule-based regression models
decision_treeDecision trees
descriptorsData Set Characteristics Available when Fitting Models
details_auto_ml_h2oAutomatic machine learning via h2o
details_bag_mars_earthBagged MARS via earth
details_bag_mlp_nnetBagged neural networks via nnet
details_bag_tree_C5.0Bagged trees via C5.0
details_bag_tree_rpartBagged trees via rpart
details_bart_dbartsBayesian additive regression trees via dbarts
details_boost_tree_C5.0Boosted trees via C5.0
details_boost_tree_h2oBoosted trees via h2o
details_boost_tree_lightgbmBoosted trees via lightgbm
details_boost_tree_mboostBoosted trees
details_boost_tree_sparkBoosted trees via Spark
details_boost_tree_xgboostBoosted trees via xgboost
details_C5_rules_C5.0C5.0 rule-based classification models
details_cubist_rules_CubistCubist rule-based regression models
details_decision_tree_C5.0Decision trees via C5.0
details_decision_tree_partykitDecision trees via partykit
details_decision_tree_rpartDecision trees via CART
details_decision_tree_sparkDecision trees via Spark
details_discrim_flexible_earthFlexible discriminant analysis via earth
details_discrim_linear_MASSLinear discriminant analysis via MASS
details_discrim_linear_mdaLinear discriminant analysis via flexible discriminant...
details_discrim_linear_sdaLinear discriminant analysis via James-Stein-type shrinkage...
details_discrim_linear_sparsediscrimLinear discriminant analysis via regularization
details_discrim_quad_MASSQuadratic discriminant analysis via MASS
details_discrim_quad_sparsediscrimQuadratic discriminant analysis via regularization
details_discrim_regularized_klaRRegularized discriminant analysis via klaR
details_gen_additive_mod_mgcvGeneralized additive models via mgcv
details_linear_reg_bruleeLinear regression via brulee
details_linear_reg_geeLinear regression via generalized estimating equations (GEE)
details_linear_reg_glmLinear regression via glm
details_linear_reg_glmerLinear regression via generalized mixed models
details_linear_reg_glmnetLinear regression via glmnet
details_linear_reg_glsLinear regression via generalized least squares
details_linear_reg_h2oLinear regression via h2o
details_linear_reg_kerasLinear regression via keras/tensorflow
details_linear_reg_lmLinear regression via lm
details_linear_reg_lmeLinear regression via mixed models
details_linear_reg_lmerLinear regression via mixed models
details_linear_reg_sparkLinear regression via spark
details_linear_reg_stanLinear regression via Bayesian Methods
details_linear_reg_stan_glmerLinear regression via hierarchical Bayesian methods
details_logistic_reg_bruleeLogistic regression via brulee
details_logistic_reg_geeLogistic regression via generalized estimating equations...
details_logistic_reg_glmLogistic regression via glm
details_logistic_reg_glmerLogistic regression via mixed models
details_logistic_reg_glmnetLogistic regression via glmnet
details_logistic_reg_h2oLogistic regression via h2o
details_logistic_reg_kerasLogistic regression via keras
details_logistic_reg_LiblineaRLogistic regression via LiblineaR
details_logistic_reg_sparkLogistic regression via spark
details_logistic_reg_stanLogistic regression via stan
details_logistic_reg_stan_glmerLogistic regression via hierarchical Bayesian methods
details_mars_earthMultivariate adaptive regression splines (MARS) via earth
details_mlp_bruleeMultilayer perceptron via brulee
details_mlp_h2oMultilayer perceptron via h2o
details_mlp_kerasMultilayer perceptron via keras
details_mlp_nnetMultilayer perceptron via nnet
details_multinom_reg_bruleeMultinomial regression via brulee
details_multinom_reg_glmnetMultinomial regression via glmnet
details_multinom_reg_h2oMultinomial regression via h2o
details_multinom_reg_kerasMultinomial regression via keras
details_multinom_reg_nnetMultinomial regression via nnet
details_multinom_reg_sparkMultinomial regression via spark
details_naive_Bayes_h2oNaive Bayes models via naivebayes
details_naive_Bayes_klaRNaive Bayes models via klaR
details_naive_Bayes_naivebayesNaive Bayes models via naivebayes
details_nearest_neighbor_kknnK-nearest neighbors via kknn
details_pls_mixOmicsPartial least squares via mixOmics
details_poisson_reg_geePoisson regression via generalized estimating equations (GEE)
details_poisson_reg_glmPoisson regression via glm
details_poisson_reg_glmerPoisson regression via mixed models
details_poisson_reg_glmnetPoisson regression via glmnet
details_poisson_reg_h2oPoisson regression via h2o
details_poisson_reg_hurdlePoisson regression via pscl
details_poisson_reg_stanPoisson regression via stan
details_poisson_reg_stan_glmerPoisson regression via hierarchical Bayesian methods
details_poisson_reg_zeroinflPoisson regression via pscl
details_proportional_hazards_glmnetProportional hazards regression
details_proportional_hazards_survivalProportional hazards regression
details_rand_forest_aorsfOblique random survival forests via aorsf
details_rand_forest_h2oRandom forests via h2o
details_rand_forest_partykitRandom forests via partykit
details_rand_forest_randomForestRandom forests via randomForest
details_rand_forest_rangerRandom forests via ranger
details_rand_forest_sparkRandom forests via spark
details_rule_fit_h2oRuleFit models via h2o
details_rule_fit_xrfRuleFit models via xrf
details_survival_reg_flexsurvParametric survival regression
details_survival_reg_flexsurvsplineFlexible parametric survival regression
details_survival_reg_survivalParametric survival regression
details_surv_reg_flexsurvParametric survival regression
details_surv_reg_survivalParametric survival regression
details_svm_linear_kernlabLinear support vector machines (SVMs) via kernlab
details_svm_linear_LiblineaRLinear support vector machines (SVMs) via LiblineaR
details_svm_poly_kernlabPolynomial support vector machines (SVMs) via kernlab
details_svm_rbf_kernlabRadial basis function support vector machines (SVMs) via...
discrim_flexibleFlexible discriminant analysis
discrim_linearLinear discriminant analysis
discrim_quadQuadratic discriminant analysis
discrim_regularizedRegularized discriminant analysis
doc-toolsTools for documenting engines
dot-model_param_name_keyTranslate names of model tuning parameters
eval_argsEvaluate parsnip model arguments
extension-check-helpersModel Specification Checking:
extract-parsnipExtract elements of a parsnip model object
fitFit a Model Specification to a Dataset
fit_controlControl the fit function
format-internalsInternal functions that format predictions
gen_additive_modGeneralized additive models (GAMs)
get_model_envWorking with the parsnip model environment
glance.model_fitConstruct a single row summary "glance" of a model, fit, or...
glm_groupedFit a grouped binomial outcome from a data set with case...
glmnet-detailsTechnical aspects of the glmnet model
glmnet_helpersHelper functions for checking the penalty of glmnet models
glmnet_helpers_predictionOrganize glmnet predictions
has_multi_predictTools for models that predict on sub-models
keras_mlpSimple interface to MLP models via keras
keras_predict_classesWrapper for keras class predictions
knit_engine_docsKnit engine-specific documentation
linear_regLinear regression
list_md_problemsLocate and show errors/warnings in engine-specific...
logistic_regLogistic regression
make_callMake a parsnip call expression
make_classesPrepend a new class
marsMultivariate adaptive regression splines (MARS)
max_mtry_formulaDetermine largest value of mtry from formula. This function...
maybe_matrixFuzzy conversions
min_colsExecution-time data dimension checks
mlpSingle layer neural network
model_dbparsnip model specification database
model_fitModel Fit Object Information
model_printerPrint helper for model objects
model_specModel Specification Information
multinom_regMultinomial regression
multi_predictModel predictions across many sub-models
naive_BayesNaive Bayes models
nearest_neighborK-nearest neighbors
nullmodelFit a simple, non-informative model
null_modelNull model
other_predictOther predict methods.
parsnip_addinStart an RStudio Addin that can write model specifications
parsnip-packageparsnip
parsnip_updateUpdating a model specification
plsPartial least squares (PLS)
poisson_regPoisson regression models
predict.model_fitModel predictions
prepare_dataPrepare data based on parsnip encoding information
proportional_hazardsProportional hazards regression
rand_forestRandom forest
reexportsObjects exported from other packages
repair_callRepair a model call object
req_pkgsDetermine required packages for a model
required_pkgs.model_specDetermine required packages for a model
rpart_trainDecision trees via rpart
rule_fitRuleFit models
set_argsChange elements of a model specification
set_engineDeclare a computational engine and specific arguments
set_new_modelTools to Register Models
set_tf_seedSet seed in R and TensorFlow at the same time
show_callPrint the model call
show_enginesDisplay currently available engines for a model
stan_conf_intWrapper for stan confidence intervals
survival_regParametric survival regression
surv_regParametric survival regression
svm_linearLinear support vector machines
svm_polyPolynomial support vector machines
svm_rbfRadial basis function support vector machines
tidy._elnettidy methods for glmnet models
tidy._LiblineaRtidy methods for LiblineaR models
tidy.model_fitTurn a parsnip model object into a tidy tibble
tidy.nullmodelTidy method for null models
translateResolve a Model Specification for a Computational Engine
type_sum.model_specSuccinct summary of parsnip object
update_model_info_fileSave information about models
varyingA placeholder function for argument values
varying_argsDetermine varying arguments
xgb_trainBoosted trees via xgboost
parsnip documentation built on Nov. 11, 2022, 9:09 a.m.