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Contributing to NetworkCausalTree

Thank you for your interest in contributing to NetworkCausalTree! We welcome contributions from the community and appreciate your efforts to improve this package.

Getting Started

NetworkCausalTree is implemented in R and follows standard R development conventions. Before contributing, please:

  1. Read our Code of Conduct
  2. Check the GitHub Issues page for existing issues or feature requests
  3. Review the package documentation and vignettes to understand the current functionality

Types of Contributions

We welcome various types of contributions:

Bug Reports

If you find a bug, please report it by:

  1. Opening a new issue on our GitHub Issues page
  2. Including a clear title and description
  3. Providing a minimal reproducible example (reprex)
  4. Specifying your R version, operating system, and package version
  5. Describing the expected vs. actual behavior

Feature Requests

Have an idea for a new feature? We'd love to hear it! Please:

  1. Check if a similar request already exists in the Issues
  2. Open a new issue with the "enhancement" label
  3. Clearly describe the feature and its potential use cases
  4. Explain how it would benefit users of the package

Code Contributions

Setting Up Your Development Environment

# Install development dependencies
install.packages(c("devtools", "roxygen2", "testthat", "knitr"))

# Clone the repository
git clone https://github.com/fbargaglistoffi/NetworkCausalTree.git
cd NetworkCausalTree

# Load the package for development
devtools::load_all()

# Run tests
devtools::test()

# Check package
devtools::check()

Contribution Workflow

  1. Fork the repository on GitHub
  2. Clone your fork locally: 
    git clone https://github.com/YOUR-USERNAME/NetworkCausalTree.git
  3. Create a new branch for your feature or bug fix: 
    git checkout -b feature/your-feature-name
  4. Make your changes following our coding standards (see below)
  5. Write or update tests to cover your changes
  6. Update documentation as needed (including roxygen2 comments)
  7. Run checks to ensure everything passes: 
    devtools::check()
devtools::test()
  8. Commit your changes with clear, descriptive commit messages: 
    git commit -m "Add feature: brief description of what you did"
  9. Push to your fork: 
    git push origin feature/your-feature-name
  10. Submit a Pull Request on GitHub with a clear description of your changes

Documentation Improvements

Documentation improvements are always welcome! This includes:

  • Fixing typos or clarifying existing documentation
  • Adding examples to function documentation
  • Improving or creating vignettes
  • Updating the README

To update function documentation:

  1. Edit the roxygen2 comments in the R source files
  2. Run devtools::document() to regenerate the documentation
  3. Preview changes with ?function_name

Vignettes and Examples

We encourage contributions that improve reproducibility:

  • Add example scripts demonstrating specific use cases
  • Create or improve vignettes showing real-world applications
  • Provide datasets that illustrate network causal inference scenarios

Coding Standards

To maintain consistency across the codebase, please follow these guidelines:

R Style Guide

  • Follow the tidyverse style guide
  • Use meaningful variable and function names
  • Keep functions focused on a single task
  • Use <- for assignment, not =
  • Indent with 2 spaces (no tabs)

Function Documentation

All exported functions must include roxygen2 documentation with:

  • @title and @description
  • @param for each parameter
  • @return describing the return value
  • @examples showing usage
  • @export if the function should be user-facing

Example:

#' @title Estimate Network Causal Effects
#' @description Estimates treatment effects under network interference
#' @param X Matrix of covariates
#' @param Y Outcome vector
#' @param W Treatment assignment vector
#' @return A list containing estimated effects and standard errors
#' @examples
#' result <- estimate_effects(X, Y, W)
#' @export
estimate_effects <- function(X, Y, W) {
  # Function implementation
}

Testing

  • All new features should include tests using testthat
  • Tests should be placed in the tests/testthat/ directory
  • Aim for good coverage of edge cases and error conditions
  • Use expect_equal(), expect_error(), etc. for assertions

Example test structure:

test_that("estimate_effects works correctly", {
  # Setup test data
  X <- matrix(rnorm(100), ncol = 2)
  Y <- rnorm(50)
  W <- rbinom(50, 1, 0.5)
  
  # Run function
  result <- estimate_effects(X, Y, W)
  
  # Check results
  expect_type(result, "list")
  expect_true("effects" %in% names(result))
})

Development Priorities

We are particularly interested in contributions related to:

  1. Continuous Exposures: Extending the package to handle multiple dosage levels (e.g., vaccine dosage)
  2. Ensemble Methods: Integration with random forest estimators for ensemble-based causal discovery (similar to grf building on causalTree)
  3. Diagnostic Tools: Developing sensitivity analysis tools under partial interference
  4. CNI Assumption Testing: Tools to assess whether the Conditional Network Independence assumption is reasonable for a given dataset
  5. Performance Optimization: Improving computational efficiency for large networks
  6. Visualization Tools: Enhanced plotting and visualization capabilities

Questions and Support

If you have questions or need support:

  1. Check the package vignettes and documentation first
  2. Search existing GitHub Issues
  3. If your question hasn't been addressed, open a new issue with the "question" label

Recognition

Contributors will be acknowledged in:

  • The package DESCRIPTION file (for significant contributions)
  • Release notes
  • The project README

License

By contributing to NetworkCausalTree, you agree that your contributions will be licensed under the same license as the project (see LICENSE file).

Thank You!

Your contributions help make NetworkCausalTree better for everyone. We appreciate your time and effort in improving this package!