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parsnip (version 1.0.2)

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

35,023

Version

1.0.2

License

MIT + file LICENSE

Issues

Pull Requests

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Repository

https://github.com/tidymodels/parsnip

Homepage

https://parsnip.tidymodels.org/

Maintainer

Max Kuhn

Last Published

October 1st, 2022

Functions in parsnip (1.0.2)

Contrast function for one-hot encodings

control_parsnip

Control the fit function

autoplot.model_fit

Create a ggplot for a model object

Ensembles of MARS models

Developer functions for predictions via BART models

A wrapper function for conditional inference tree models

Cubist rule-based regression models

Bayesian additive regression trees (BART)

details_bag_tree_C5.0

Bagged trees via C5.0

details_bag_tree_rpart

Bagged trees via rpart

convert_stan_interval

Convenience function for intervals

.convert_form_to_xy_fit

Helper functions to convert between formula and matrix interface

Data Set Characteristics Available when Fitting Models

Using case weights with parsnip

details_bag_mlp_nnet

Bagged neural networks via nnet

details_bag_mars_earth

Bagged MARS via earth

details_C5_rules_C5.0

C5.0 rule-based classification models

details_auto_ml_h2o

Automatic machine learning via h2o

details_cubist_rules_Cubist

Cubist rule-based regression models

details_boost_tree_xgboost

Boosted trees via xgboost

Automatic Machine Learning

augment.model_fit

Augment data with predictions

details_boost_tree_h2o

Boosted trees via h2o

details_boost_tree_lightgbm

Boosted trees via lightgbm

details_bart_dbarts

Bayesian additive regression trees via dbarts

details_boost_tree_C5.0

Boosted trees via C5.0

details_discrim_linear_sparsediscrim

Linear discriminant analysis via regularization

details_discrim_quad_MASS

Quadratic discriminant analysis via MASS

details_discrim_quad_sparsediscrim

Quadratic discriminant analysis via regularization

details_discrim_regularized_klaR

Regularized discriminant analysis via klaR

condense_control

Condense control object into strictly smaller control object

check_empty_ellipse

Check to ensure that ellipses are empty

details_discrim_flexible_earth

Flexible discriminant analysis via earth

details_discrim_linear_sda

Linear discriminant analysis via James-Stein-type shrinkage estimation

details_discrim_linear_mda

Linear discriminant analysis via flexible discriminant analysis

details_decision_tree_C5.0

Decision trees via C5.0

details_decision_tree_partykit

Decision trees via partykit

details_decision_tree_rpart

Decision trees via CART

details_boost_tree_mboost

details_decision_tree_spark

Decision trees via Spark

details_gen_additive_mod_mgcv

Generalized additive models via mgcv

details_linear_reg_glmnet

Linear regression via glmnet

details_linear_reg_glmer

Linear regression via generalized mixed models

details_linear_reg_brulee

Linear regression via brulee

details_discrim_linear_MASS

Linear discriminant analysis via MASS

details_linear_reg_gls

Linear regression via generalized least squares

details_linear_reg_glm

Linear regression via glm

details_linear_reg_gee

Linear regression via generalized estimating equations (GEE)

details_linear_reg_h2o

Linear regression via h2o

details_logistic_reg_glm

Logistic regression via glm

details_boost_tree_spark

Boosted trees via Spark

details_linear_reg_lme

Linear regression via mixed models

details_linear_reg_lm

Linear regression via lm

details_linear_reg_keras

Linear regression via keras/tensorflow

details_linear_reg_lmer

Linear regression via mixed models

details_logistic_reg_LiblineaR

Logistic regression via LiblineaR

details_linear_reg_stan_glmer

Linear regression via hierarchical Bayesian methods

details_logistic_reg_glmer

Logistic regression via mixed models

details_naive_Bayes_h2o

Naive Bayes models via naivebayes

details_mlp_keras

Multilayer perceptron via keras

details_logistic_reg_keras

Logistic regression via keras

details_mlp_h2o

Multilayer perceptron via h2o

details_logistic_reg_spark

Logistic regression via spark

details_multinom_reg_spark

Multinomial regression via spark

details_linear_reg_spark

Linear regression via spark

details_linear_reg_stan

Linear regression via Bayesian Methods

details_logistic_reg_stan

Logistic regression via stan

details_logistic_reg_stan_glmer

Logistic regression via hierarchical Bayesian methods

details_multinom_reg_glmnet

Multinomial regression via glmnet

details_mlp_brulee

Multilayer perceptron via brulee

details_mars_earth

Multivariate adaptive regression splines (MARS) via earth

details_mlp_nnet

Multilayer perceptron via nnet

details_multinom_reg_h2o

Multinomial regression via h2o

details_poisson_reg_stan_glmer

Poisson regression via hierarchical Bayesian methods

details_naive_Bayes_klaR

Naive Bayes models via klaR

details_poisson_reg_stan

Poisson regression via stan

details_logistic_reg_gee

Logistic regression via generalized estimating equations (GEE)

details_naive_Bayes_naivebayes

Naive Bayes models via naivebayes

details_logistic_reg_brulee

Logistic regression via brulee

details_poisson_reg_gee

Poisson regression via generalized estimating equations (GEE)

details_poisson_reg_glm

Poisson regression via glm

details_poisson_reg_h2o

Poisson regression via h2o

details_rule_fit_xrf

RuleFit models via xrf

details_rule_fit_h2o

RuleFit models via h2o

details_rand_forest_partykit

Random forests via partykit

details_multinom_reg_brulee

Multinomial regression via brulee

details_poisson_reg_zeroinfl

Poisson regression via pscl

details_proportional_hazards_glmnet

Proportional hazards regression

details_rand_forest_randomForest

Random forests via randomForest

details_poisson_reg_hurdle

Poisson regression via pscl

details_svm_linear_LiblineaR

Linear support vector machines (SVMs) via LiblineaR

details_rand_forest_ranger

Random forests via ranger

details_logistic_reg_h2o

Logistic regression via h2o

details_logistic_reg_glmnet

Logistic regression via glmnet

gen_additive_mod

Generalized additive models (GAMs)

details_svm_poly_kernlab

Polynomial support vector machines (SVMs) via kernlab

details_svm_rbf_kernlab

Radial basis function support vector machines (SVMs) via kernlab

Tools for documenting engines

details_svm_linear_kernlab

Linear support vector machines (SVMs) via kernlab

details_survival_reg_flexsurv

Parametric survival regression

details_rand_forest_spark

Random forests via spark

.model_param_name_key

Translate names of model tuning parameters

format-internals

Internal functions that format predictions

Working with the parsnip model environment

details_multinom_reg_keras

Multinomial regression via keras

details_survival_reg_survival

Parametric survival regression

details_poisson_reg_glmer

Poisson regression via mixed models

Evaluate parsnip model arguments

Multivariate adaptive regression splines (MARS)

discrim_flexible

Flexible discriminant analysis

extract-parsnip

Extract elements of a parsnip model object

Prepend a new class

details_poisson_reg_glmnet

Poisson regression via glmnet

details_multinom_reg_nnet

Multinomial regression via nnet

details_rand_forest_h2o

Random forests via h2o

Quadratic discriminant analysis

keras_predict_classes

Wrapper for keras class predictions

details_proportional_hazards_survival

Proportional hazards regression

details_pls_mixOmics

Partial least squares via mixOmics

details_nearest_neighbor_kknn

K-nearest neighbors via kknn

knit_engine_docs

Knit engine-specific documentation

Linear discriminant analysis

nearest_neighbor

K-nearest neighbors

Naive Bayes models

details_surv_reg_flexsurv

Parametric survival regression

Control the fit function

Fit a Model Specification to a Dataset

Linear regression

discrim_regularized

Regularized discriminant analysis

Simple interface to MLP models via keras

Logistic regression

list_md_problems

Locate and show errors/warnings in engine-specific documentation

has_multi_predict

Tools for models that predict on sub-models

Make a parsnip call expression

Print helper for model objects

predict_class.model_fit

Other predict methods.

glance.model_fit

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

.check_glmnet_penalty_fit

Helper functions for checking the penalty of glmnet models

details_surv_reg_survival

Parametric survival regression

Poisson regression models

Model Specification Information

Partial least squares (PLS)

parsnip-package

Turn a parsnip model object into a tidy tibble

Determine required packages for a model

required_pkgs.model_spec

Determine required packages for a model

tidy._LiblineaR

tidy methods for LiblineaR models

Tidy method for null models

tidy methods for glmnet models

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

proportional_hazards

Proportional hazards regression

Resolve a Model Specification for a Computational Engine

Model Fit Object Information

Set seed in R and TensorFlow at the same time

check_model_exists

Tools to Register Models

Execution-time data dimension checks

Single layer neural network

parsnip model specification database

Technical aspects of the glmnet model

.organize_glmnet_pred

Organize glmnet predictions

type_sum.model_spec

Succinct summary of parsnip object

Declare a computational engine and specific arguments

Change elements of a model specification

Display currently available engines for a model

Objects exported from other packages

Print the model call

Model predictions across many sub-models

Parametric survival regression

Repair a model call object

A placeholder function for argument values

Wrapper for stan confidence intervals

update_model_info_file

Save information about models

Start an RStudio Addin that can write model specifications

Multinomial regression

update.bag_mars

Updating a model specification

Fuzzy conversions

Fit a simple, non-informative model

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.

Parametric survival regression

Decision trees via rpart

Polynomial support vector machines

Prepare data based on parsnip encoding information

Boosted trees via xgboost

predict.model_fit

Model predictions

Linear support vector machines

varying_args.model_spec

Determine varying arguments

Radial basis function support vector machines

C5.0 rule-based classification models

Ensembles of neural networks

Ensembles of decision trees

Add a column of row numbers to a data frame

Boosted trees via C5.0

Functions required for parsnip-adjacent packages