tidymodels/model-implementation-principles

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tidymodels/model-implementation-principles

tidymodels

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recommendations for creating R modeling packages

broom

Convert statistical analysis objects from R into tidy format

tidymodels

Easily install and load the tidymodels packages

infer

An R package for tidyverse-friendly statistical inference

parsnip

A tidy unified interface to models

corrr

Explore correlations in R

TMwR

Code and content for "Tidy Modeling with R"

recipes

Pipeable steps for feature engineering and data preprocessing to prepare for modeling

yardstick

Tidy methods for measuring model performance

rsample

Classes and functions to create and summarize resampling objects

stacks

An R package for tidy stacked ensemble modeling

tune

Tools for tidy parameter tuning

tidypredict

Run predictions inside the database

workflows

Modeling Workflows

textrecipes

Extra recipes for Text Processing

themis

Extra recipes steps for dealing with unbalanced data

embed

Extra recipes for predictor embeddings

butcher

Reduce the size of model objects saved to disk

censored

Parsnip wrappers for survival models

probably

Tools for post-processing class probability estimates

dials

Tools for creating tuning parameter values

tidyclust

A tidy unified interface to clustering models

tidyposterior

Bayesian comparisons of models using resampled statistics

hardhat

Construct Modeling Packages

aml-training

The most recent version of the Applied Machine Learning notes

tidymodels.org-legacy

Legacy Source of tidymodels.org

workshops

Website and materials for tidymodels workshops

workflowsets

Create a collection of modeling workflows

usemodels

Boilerplate Code for tidymodels

modeldb

Run models inside a database using R

multilevelmod

Parsnip wrappers for mixed-level and hierarchical models

spatialsample

Create and summarize spatial resampling objects 🗺

learntidymodels

Learn tidymodels with interactive learnr primers

brulee

High-Level Modeling Functions with 'torch'

finetune

Additional functions for model tuning

bonsai

parsnip wrappers for tree-based models

shinymodels

applicable

Quantify extrapolation of new samples given a training set

rules

parsnip extension for rule-based models

planning

Documents to plan and discuss future development

discrim

Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package

baguette

parsnip Model Functions for Bagging

modeldata

Data Sets Used by tidymodels Packages

poissonreg

parsnip wrappers for Poisson regression

agua

Create and evaluate models using 'tidymodels' and 'h2o'

extratests

Integration and other testing for tidymodels

tidymodels.org

Source of tidymodels.org

plsmod

Model Wrappers for Projection Methods

cloudstart

RStudio Cloud ☁️ resources to accompany tidymodels.org

orbital

Turn Tidymodels Workflows Into Series of Equations

desirability2

Desirability Functions for Multiparameter Optimization

modeldatatoo

More Data Sets Useful for Modeling Examples

.github

GitHub contributing guidelines for tidymodels packages

modelenv

Provide Tools to Register Models for use in Tidymodels

tailor

Sandbox for a postprocessor object.

survivalauc

What the Package Does (One Line, Title Case)