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parsnip

A Common API to Modeling and Analysis Functions

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).

Functions (60)

C5.0_train

Boosted trees via C5.0

add_on_exports

Functions required for parsnip-adjacent packages

add_rowindex

Add a column of row numbers to a data frame

augment.model_fit

Augment data with predictions

boost_tree

General Interface for Boosted Trees

check_empty_ellipse

Check to ensure that ellipses are empty

contr_one_hot

Contrast function for one-hot encodings

control_parsnip

Control the fit function

convert_stan_interval

Convenience function for intervals

decision_tree

General Interface for Decision Tree Models

descriptors

Data Set Characteristics Available when Fitting Models

eval_args

Evaluate parsnip model arguments

fit

Fit a Model Specification to a Dataset

get_model_env

Working with the parsnip model environment

glance.model_fit

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

has_multi_predict

Tools for models that predict on sub-models

keras_mlp

Simple interface to MLP models via keras

linear_reg

General Interface for Linear Regression Models

logistic_reg

General Interface for Logistic Regression Models

make_call

Make a parsnip call expression

make_classes

Prepend a new class

mars

General Interface for MARS

maybe_matrix

Fuzzy conversions

min_cols

Execution-time data dimension checks

mlp

General Interface for Single Layer Neural Network

model_db

parsnip model specification database

model_fit

Model Fit Object Information

model_printer

Print helper for model objects

model_spec

Model Specification Information

multi_predict

Model predictions across many sub-models

multinom_reg

General Interface for Multinomial Regression Models

nearest_neighbor

General Interface for K-Nearest Neighbor Models

null_model

General Interface for null models

nullmodel

Fit a simple, non-informative model

other_predict

Other predict methods.

parsnip_addin

Start an RStudio Addin that can write model specifications

predict.model_fit

Model predictions

prepare_data

Prepare data based on parsnip encoding information

rand_forest

General Interface for Random Forest Models

reexports

Objects exported from other packages

repair_call

Repair a model call object

req_pkgs

Determine required packages for a model

rpart_train

Decision trees via rpart

set_args

Change elements of a model specification

set_engine

Declare a computational engine and specific arguments

set_new_model

Tools to Register Models

show_call

Print the model call

show_engines

Display currently available engines for a model

stan_conf_int

Wrapper for stan confidence intervals

surv_reg

General Interface for Parametric Survival Models

svm_poly

General interface for polynomial support vector machines

svm_rbf

General interface for radial basis function support vector machines

tidy._elnet

tidy methods for glmnet models

tidy.model_fit

Turn a parsnip model object into a tidy tibble

tidy.nullmodel

Tidy method for null models

translate

Resolve a Model Specification for a Computational Engine

type_sum.model_spec

Succinct summary of parsnip object

varying

A placeholder function for argument values

varying_args.model_spec

Determine varying arguments

xgb_train

Boosted trees via xgboost

parsnip

A Common API to Modeling and Analysis Functions

v0.1.5

GPL-2

Authors

Max Kuhn [aut, cre], Davis Vaughan [aut], RStudio [cph]

Initial release

parsnip

Functions (60)

parsnip

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