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parsnip (version 1.0.0)

A Common API to Modeling and Analysis Functions

Description

A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).

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Install

install.packages('parsnip')

Monthly Downloads

38,321

Version

1.0.0

License

MIT + file LICENSE

Issues

86

Pull Requests

3

Stars

624

Forks

92

Maintainer

Max Kuhn

Last Published

June 16th, 2022

Functions in parsnip (1.0.0)

augment.model_fit

Augment data with predictions
control_parsnip

Control the fit function
autoplot.model_fit

Create a ggplot for a model object
ctree_train

A wrapper function for conditional inference tree models
descriptors

Data Set Characteristics Available when Fitting Models
convert_stan_interval

Convenience function for intervals
.convert_form_to_xy_fit

Helper functions to convert between formula and matrix interface
bag_tree

Ensembles of decision trees
bag_mars

Ensembles of MARS models
details_C5_rules_C5.0

C5.0 rule-based classification models
details_boost_tree_C5.0

Boosted trees via C5.0
details_boost_tree_h2o

Boosted trees via h2o
details_decision_tree_partykit

Decision trees via partykit
details_decision_tree_spark

Decision trees via Spark
details_discrim_flexible_earth

Flexible discriminant analysis via earth
details_decision_tree_rpart

Decision trees via CART
details_discrim_linear_sda

Linear discriminant analysis via James-Stein-type shrinkage estimation
details_linear_reg_glm

Linear regression via glm
details_linear_reg_glmnet

Linear regression via glmnet
null_value

Functions required for parsnip-adjacent packages
details_discrim_quad_MASS

Quadratic discriminant analysis via MASS
details_bag_tree_rpart

Bagged trees via rpart
details_bart_dbarts

Bayesian additive regression trees via dbarts
details_discrim_linear_mda

Linear discriminant analysis via flexible discriminant analysis
details_discrim_linear_MASS

Linear discriminant analysis via MASS
add_rowindex

Add a column of row numbers to a data frame
details_discrim_linear_sparsediscrim

Linear discriminant analysis via regularization
details_linear_reg_gls

Linear regression via generalized least squares
details_linear_reg_h2o

Linear regression via h2o
C5_rules

C5.0 rule-based classification models
boost_tree

Boosted trees
C5.0_train

Boosted trees via C5.0
details_linear_reg_stan

Linear regression via Bayesian Methods
details_linear_reg_spark

Linear regression via spark
details_logistic_reg_brulee

Logistic regression via brulee
details_multinom_reg_glmnet

Multinomial regression via glmnet
details_logistic_reg_stan

Logistic regression via stan
details_logistic_reg_stan_glmer

Logistic regression via hierarchical Bayesian methods
details_multinom_reg_h2o

Multinomial regression via h2o
details_logistic_reg_glmnet

Logistic regression via glmnet
details_logistic_reg_h2o

Logistic regression via h2o
details_discrim_quad_sparsediscrim

Quadratic discriminant analysis via regularization
details_multinom_reg_keras

Multinomial regression via keras
cubist_rules

Cubist rule-based regression models
decision_tree

Decision trees
check_empty_ellipse

Check to ensure that ellipses are empty
bart

Bayesian additive regression trees (BART)
bart-internal

Developer functions for predictions via BART models
details_multinom_reg_nnet

Multinomial regression via nnet
details_logistic_reg_gee

Logistic regression via generalized estimating equations (GEE)
contr_one_hot

Contrast function for one-hot encodings
details_bag_mars_earth

Bagged MARS via earth
details_mlp_h2o

Multilayer perceptron via h2o
details_boost_tree_lightgbm

Boosted trees via lightgbm
details_mlp_keras

Multilayer perceptron via keras
details_poisson_reg_glmer

Poisson regression via mixed models
case_weights

Using case weights with parsnip
details_boost_tree_mboost

Boosted trees
details_rand_forest_partykit

Random forests via partykit
details_bag_tree_C5.0

Bagged trees via C5.0
details_poisson_reg_glmnet

Poisson regression via glmnet
details_linear_reg_keras

Linear regression via keras/tensorflow
details_logistic_reg_glmer

Logistic regression via mixed models
details_mars_earth

Multivariate adaptive regression splines (MARS) via earth
details_linear_reg_lm

Linear regression via lm
details_linear_reg_gee

Linear regression via generalized estimating equations (GEE)
details_linear_reg_brulee

Linear regression via brulee
details_boost_tree_spark

Boosted trees via Spark
details_logistic_reg_glm

Logistic regression via glm
details_mlp_brulee

Multilayer perceptron via brulee
details_multinom_reg_spark

Multinomial regression via spark
details_survival_reg_flexsurv

Parametric survival regression
details_proportional_hazards_survival

Proportional hazards regression
details_survival_reg_survival

Parametric survival regression
details_naive_Bayes_h2o

Naive Bayes models via naivebayes
details_rand_forest_h2o

Random forests via h2o
details_svm_linear_kernlab

Linear support vector machines (SVMs) via kernlab
details_rand_forest_randomForest

Random forests via randomForest
details_svm_linear_LiblineaR

Linear support vector machines (SVMs) via LiblineaR
details_boost_tree_xgboost

Boosted trees via xgboost
doc-tools

Tools for documenting engines
details_discrim_regularized_klaR

Regularized discriminant analysis via klaR
details_surv_reg_survival

Parametric survival regression
discrim_flexible

Flexible discriminant analysis
details_surv_reg_flexsurv

Parametric survival regression
details_poisson_reg_zeroinfl

Poisson regression via pscl
details_proportional_hazards_glmnet

Proportional hazards regression
details_gen_additive_mod_mgcv

Generalized additive models via mgcv
.check_glmnet_penalty_fit

Helper functions for checking the penalty of glmnet models
.organize_glmnet_pred

Organize glmnet predictions
details_linear_reg_stan_glmer

Linear regression via hierarchical Bayesian methods
multinom_reg

Multinomial regression
multi_predict

Model predictions across many sub-models
keras_mlp

Simple interface to MLP models via keras
has_multi_predict

Tools for models that predict on sub-models
details_poisson_reg_h2o

Poisson regression via h2o
details_poisson_reg_glm

Poisson regression via glm
details_poisson_reg_gee

Poisson regression via generalized estimating equations (GEE)
details_poisson_reg_hurdle

Poisson regression via pscl
parsnip_addin

Start an RStudio Addin that can write model specifications
.model_param_name_key

Translate names of model tuning parameters
details_logistic_reg_keras

Logistic regression via keras
details_logistic_reg_LiblineaR

Logistic regression via LiblineaR
details_logistic_reg_spark

Logistic regression via spark
update.bag_mars

Updating a model specification
rule_fit

RuleFit models
rpart_train

Decision trees via rpart
details_naive_Bayes_naivebayes

Naive Bayes models via naivebayes
details_naive_Bayes_klaR

Naive Bayes models via klaR
details_poisson_reg_stan

Poisson regression via stan
details_mlp_nnet

Multilayer perceptron via nnet
details_cubist_rules_Cubist

Cubist rule-based regression models
details_multinom_reg_brulee

Multinomial regression via brulee
details_poisson_reg_stan_glmer

Poisson regression via hierarchical Bayesian methods
set_args

Change elements of a model specification
set_engine

Declare a computational engine and specific arguments
keras_predict_classes

Wrapper for keras class predictions
mlp

Single layer neural network
min_cols

Execution-time data dimension checks
knit_engine_docs

Knit engine-specific documentation
details_rule_fit_h2o

RuleFit models via h2o
show_call

Print the model call
eval_args

Evaluate parsnip model arguments
details_rule_fit_xrf

RuleFit models via xrf
discrim_linear

Linear discriminant analysis
get_model_env

Working with the parsnip model environment
format-internals

Internal functions that format predictions
gen_additive_mod

Generalized additive models (GAMs)
discrim_regularized

Regularized discriminant analysis
discrim_quad

Quadratic discriminant analysis
logistic_reg

Logistic regression
show_engines

Display currently available engines for a model
mars

Multivariate adaptive regression splines (MARS)
make_classes

Prepend a new class
glance.model_fit

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

Make a parsnip call expression
fit.model_spec

Fit a Model Specification to a Dataset
extract-parsnip

Extract elements of a parsnip model object
nearest_neighbor

K-nearest neighbors
fit_control

Control the fit function
naive_Bayes

Naive Bayes models
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.
proportional_hazards

Proportional hazards regression
rand_forest

Random forest
update_model_info_file

Save information about models
details_linear_reg_lme

Linear regression via mixed models
details_nearest_neighbor_kknn

K-nearest neighbors via kknn
details_decision_tree_C5.0

Decision trees via C5.0
details_pls_mixOmics

Partial least squares via mixOmics
details_rand_forest_ranger

Random forests via ranger
details_linear_reg_lmer

Linear regression via mixed models
model_db

parsnip model specification database
varying

A placeholder function for argument values
model_fit

Model Fit Object Information
details_rand_forest_spark

Random forests via spark
predict_class.model_fit

Other predict methods.
prepare_data

Prepare data based on parsnip encoding information
predict.model_fit

Model predictions
parsnip-package

parsnip
varying_args.model_spec

Determine varying arguments
survival_reg

Parametric survival regression
tidy.nullmodel

Tidy method for null models
tidy.model_fit

Turn a parsnip model object into a tidy tibble
svm_linear

Linear support vector machines
details_svm_poly_kernlab

Polynomial support vector machines (SVMs) via kernlab
xgb_train

Boosted trees via xgboost
maybe_matrix

Fuzzy conversions
linear_reg

Linear regression
model_printer

Print helper for model objects
glm_grouped

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

Locate and show errors/warnings in engine-specific documentation
glmnet-details

Technical aspects of the glmnet model
details_svm_rbf_kernlab

Radial basis function support vector machines (SVMs) via kernlab
tidy._LiblineaR

tidy methods for LiblineaR models
tidy._elnet

tidy methods for glmnet models
model_spec

Model Specification Information
null_model

Null model
svm_rbf

Radial basis function support vector machines
pls

Partial least squares (PLS)
repair_call

Repair a model call object
stan_conf_int

Wrapper for stan confidence intervals
poisson_reg

Poisson regression models
svm_poly

Polynomial support vector machines
set_tf_seed

Set seed in R and TensorFlow at the same time
check_model_exists

Tools to Register Models
required_pkgs.model_spec

Determine required packages for a model
translate

Resolve a Model Specification for a Computational Engine
req_pkgs

Determine required packages for a model
type_sum.model_spec

Succinct summary of parsnip object
surv_reg

Parametric survival regression
reexports

Objects exported from other packages
nullmodel

Fit a simple, non-informative model