Add Parsnip Engine

Guide for adding new engines to existing parsnip models. This skill covers registering engines (like adding “spark” to linear_reg()) without creating entirely new model types.

Use this skill when: Adding a new engine to an existing parsnip model type.

For creating new models: See add-parsnip-model skill instead.

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Two Development Contexts

This skill supports two distinct development contexts:

🆕 Extension Development (Default)

Creating a new R package that adds engines to existing parsnip models.

• ✅ Use this for: New packages, standalone engines, CRAN submissions

• 📦 Package detection: No parsnip in DESCRIPTION’s Package: field

• ⚠️ Constraint: Can only use exported functions (no :::)

• 📖 Guide: Extension Development Guide

🔧 Source Development (Advanced)

Contributing directly to parsnip via pull requests.

• ✅ Use this for: Contributing to tidymodels/parsnip repository

• 📦 Package detection: Package: parsnip in DESCRIPTION

• ✨ Benefit: Can use internal functions and package infrastructure

• 📖 Guide: Source Development Guide

This main guide shows extension development patterns. If you’re contributing to parsnip itself, see the Source Development Guide for package-specific details.

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Getting Started

INSTRUCTIONS FOR CLAUDE: Run the verification script first to determine the development context:

Rscript -e 'source(Sys.glob(path.expand("~/.claude/plugins/cache/tidymodels-skills/tidymodels-dev/*/tidymodels/shared-references/scripts/verify-setup.R"))[1])'

Then follow the appropriate path based on the output:

• Output: “All checks for source development complete.” → Go to Source Development Guide

• Output: “All checks for extension development complete.” (no warnings) → Go to Extension Development Guide

• Output: Shows “Warning - [UUID]” messages → Go to Extension Prerequisites to resolve warnings first

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Overview

Adding an engine to an existing parsnip model provides:

• Connection to new computational backends (e.g., H2O, Spark, TensorFlow)

• Standardized interface with parsnip models

• Support for multiple prediction types

• Integration with tidymodels ecosystem

• Consistent API regardless of engine

What this skill covers:

• Planning and choosing the right interface

• Complete registration sequence

• Fit and predict method implementation

• Testing engine implementations

• Multi-mode support (regression + classification)

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Repository Access (Optional but Recommended)

INSTRUCTIONS FOR CLAUDE: Check if repos/parsnip/ exists in the current working directory. Use this to guide development:

If repos/parsnip/ exists:

• ✅ Use it as a reference throughout development

• Read source files (e.g., repos/parsnip/R/linear_reg_data.R) to study engine registration patterns

• Read test files (e.g., repos/parsnip/tests/testthat/test-linear_reg.R) for testing patterns

• Reference these files when answering complex questions or solving problems

• Look at actual code structure, validation patterns, and edge case handling

If repos/parsnip/ does NOT exist:

• Suggest cloning the repository using the scripts in Repository Access Guide

• This is optional but strongly recommended for high-quality development

• If the user declines, reference files using GitHub URLs:

  • Format: https://github.com/tidymodels/parsnip/blob/main/R/[file-name].R

  • Example: https://github.com/tidymodels/parsnip/blob/main/R/linear_reg_data.R

  • This allows users to click through to see implementations

When to use repository references:

• Complex implementation questions (e.g., “How does parsnip handle multi-mode engines?”)

• Debugging issues (compare user’s code to working implementation)

• Understanding patterns (study similar engines)

• Test design (see how parsnip tests edge cases)

• Architecture decisions (understand internal structure)

See Repository Access Guide for setup instructions.

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Quick Navigation

Development Guides:

• Extension Development Guide - Creating new packages that add engines

• Source Development Guide - Contributing PRs to parsnip itself

Core Implementation References:

• Engine Implementation - Complete registration sequence, examples, patterns

• Fit and Predict Methods - Implementation details for fit/predict

• Prediction Types - All 11 prediction types

• Mode Handling - Multi-mode support (regression

  • classification)

• Encoding Options - Interface types and data conversion

Model-Specific Guides:

• Model Specification System - How parsnip models work

Shared References (Extension Development):

• Extension Prerequisites - Package setup

• Development Workflow - Fast iteration cycle

• Extension Requirements - Complete guide:

  • Best Practices

  • Testing Patterns

  • Troubleshooting

• Roxygen Documentation

• Package Imports

Source Development Specific:

• Testing Patterns (Source)

• Best Practices (Source)

• Troubleshooting (Source)

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Prerequisites

⚠️ IMPORTANT: Before implementing engines, complete the extension prerequisites sequence:

👉 Extension Prerequisites Guide

This guide includes critical steps like use_claude_code() (if available) that must run BEFORE adding dependencies. Following the complete sequence ensures proper package initialization and Claude Code integration.

After completing extension prerequisites, return here to implement your engine.

Parsnip Fundamentals:

Before adding an engine, understand:

• How parsnip models work - Model Specification System

• Fit and predict patterns - Fit and Predict Methods

• Available output formats - Prediction Types

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Implementation Overview

INSTRUCTIONS FOR CLAUDE: Assess complexity first, then choose approach:

Simple Engine?

• Single mode (regression OR classification, not both)

• Formula interface OR matrix interface (pick one)

• 1-3 parameters to map

• Standard prediction type (numeric OR class/prob)

→ Use streamlined approach:

• Target 2 files: R/zzz.R (15-30 lines), tests/testthat/test-*.R; acceptable to reach 4-6 if needed

• NO summary docs, NO example files

• See Extension Guide, Simple Single-Mode

Complex Engine?

• Multi-mode (regression AND classification)

• Matrix interface with encoding

• Survival/censored regression

• Custom prediction post-processing

→ Reference detailed guides:

• See Mode Handling for multi-mode

• See Encoding Options for matrix interfaces

• Still target 2-3 files (R/zzz.R, tests, optional README); acceptable to reach 4-6 if implementation requires it

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Core registration steps:

1. Plan - Identify model, choose interface, decide on modes
2. Register - Declare engine exists with set_model_engine()
3. Dependencies - Declare packages with set_dependency()
4. Arguments - Translate main arguments with set_model_arg()
5. Fit - Register fit method with set_fit()
6. Encoding - Configure interface with set_encoding() (if needed)
7. Predict - Register prediction types with set_pred()
8. Test - Verify all interfaces and prediction types work

File Discipline:

• Extension: Create 2-3 files (R/zzz.R, tests/testthat/test-*.R, optional README.md); acceptable to reach 4-6 files if implementation requires it

• Source: Modify 1-2 files (add to R/_data.R, add to tests/testthat/test-.R); acceptable to reach 3-7 files if implementation requires it

• Never create: IMPLEMENTATION_SUMMARY.md, example_usage.R, helper files

See Engine Implementation Guide for complete details and examples.

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Registration Process

The registration process differs slightly by context:

Extension Development:

• Register in .onLoad() function

• Use parsnip:: prefix for all functions

• Cannot access internal helpers

• Create function that contains all registrations

Source Development:

• Add to existing R/[model]_data.R file

• No prefix needed for parsnip functions

• Can use internal helpers if needed

• Follow existing file organization patterns

See respective guides for detailed registration patterns.

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Testing Your Engine

Essential tests to include:

• Engine fits successfully

• Formula and xy interfaces work (if applicable)

• Each prediction type returns correct format

• Predictions match data dimensions

• Factor handling works correctly

• Error messages are clear

See testing guides:

• Extension: Testing Patterns (Extension)

• Source: Testing Patterns (Source)

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When to Add an Engine

Add an engine when:

• Model type already exists in parsnip

• Engine provides different computational approach

• Engine offers performance benefits or unique features

• Package is well-maintained and stable

Don’t add an engine when:

• Model type doesn’t exist (see add-parsnip-model instead)

• Engine is functionally identical to existing

• Package is experimental or unmaintained

• Only cosmetic differences from existing engines

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Related Skills

• add-parsnip-model - Create new model specifications (if model doesn’t exist yet)

• add-dials-parameter - Define tunable parameters for engine arguments

• add-recipe-step - Preprocess data before model fitting

• add-yardstick-metric - Evaluate engine predictions with custom metrics

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Next Steps

For Extension Development (creating new packages):

1. Complete Extension Prerequisites
2. Follow Extension Development Guide
3. Implement engine using Engine Implementation Guide
4. Test thoroughly using Testing Patterns
5. Consider contributing to parsnip

For Source Development (contributing to parsnip):

1. Clone tidymodels/parsnip repository
2. Follow Source Development Guide
3. Implement engine in appropriate R/[model]_data.R file
4. Add comprehensive tests using Testing Patterns (Source)
5. Update NEWS.md and submit PR

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For questions or contributions, see:

• Tidymodels GitHub

• Tidymodels Community