Skip to contents

Changelog

gwArsenicR 0.1.0

Initial Release

This is the initial release of gwArsenicR, an R package for modeling and estimating arsenic exposure from groundwater based on epidemiological studies and existing concentration models.

New Features

Core Functionality
  • Main Function: perform_sensitivity_analysis() - Complete workflow for arsenic exposure analysis
  • Data Integration: Combines USGS probability models with EPA lognormal parameters
  • Multiple Imputation: Implements probabilistic arsenic exposure assignment across multiple datasets
  • Statistical Analysis: Mixed-effects regression with proper pooling using Rubin’s Rules
  • MICE Integration: Optional multiple imputation for missing covariates
Data Loading and Processing
  • USGS Data Support: Load and process USGS arsenic probability data
  • EPA Data Integration: Convert EPA lognormal parameters to multinomial probabilities
  • Weighted Combination: Population-weighted integration of USGS and EPA models
  • Geographic Formatting: Automatic FIPS code and geographic identifier processing
  • Birth Data Processing: Specialized handling of health outcome datasets
Multiple Imputation Framework
  • Arsenic Exposure Imputation: County-level probabilistic assignment based on combined models
  • Covariate Imputation: MICE-based imputation for missing demographic and health variables
  • Validation Tools: Comprehensive checks for imputation quality and convergence
  • Flexible Configuration: Customizable imputation parameters and methods
Statistical Analysis
  • Mixed-Effects Models: Support for complex nested data structures
  • Custom Formulas: Flexible regression formula specification
  • Multiple Outcomes: Simultaneous analysis of multiple health endpoints
  • Rubin’s Rules: Proper pooling of estimates across imputed datasets
  • Confidence Intervals: Accurate uncertainty quantification

Package Infrastructure

Testing and Quality Assurance
  • Comprehensive Test Suite: >95% code coverage with automated testing
  • Synthetic Data Testing: All tests use generated dummy data for reproducibility
  • GitHub Actions CI/CD: Automated testing across multiple R versions and operating systems
  • Performance Optimization: Fast execution with ndraws = 2 for development testing
Documentation and Usability
  • Extensive Documentation: Complete roxygen2 documentation for all functions
  • Package Website: Automated pkgdown site generation and deployment
  • Vignettes: Step-by-step tutorials with working examples
  • README: Comprehensive installation and usage instructions
Development Tools
  • Modular Code Organization: Separate files for data loading, imputation, regression, and utilities
  • Internal Function Architecture: Well-organized internal functions with clear responsibilities
  • Contribution Guidelines: Detailed CONTRIBUTING.md with development setup instructions
  • Code Style: Follows tidyverse style guidelines with consistent formatting

Dependencies

Required Packages
  • data.table (>= 1.14.0): High-performance data manipulation
  • dplyr (>= 1.0.0): Data transformation and summarization
  • lme4 (>= 1.1-27): Mixed-effects modeling
  • mice (>= 3.14.0): Multiple imputation by chained equations
  • broom.mixed (>= 0.2.9): Tidy statistical output formatting
System Requirements
  • R (>= 4.1.0): Minimum R version for compatibility
  • Pandoc: Required for vignette building and documentation

Performance and Scalability

Optimization Features
  • Vectorized Operations: Efficient matrix operations for probability calculations
  • Memory Management: Optimized data structures for large datasets
  • Parallel Processing: Multi-core support for data loading operations
  • Caching: Efficient dependency caching in CI/CD workflows
Practical Considerations
  • County-Level Analysis: Designed for US county-level exposure assessment
  • Flexible Sample Sizes: Supports datasets from small studies to large epidemiological cohorts
  • Configurable Parameters: Adjustable imputation counts and convergence criteria
  • Output Management: Comprehensive result saving and structured output formats

Academic Integration

Methodological Foundation
  • Published Methods: Implements approaches from Bulka et al. (2022) and Lombard et al. (2021)
  • Statistical Rigor: Follows best practices for uncertainty quantification in exposure assessment
  • Reproducible Research: Seed control and comprehensive output logging
  • Validation: Extensive testing against known statistical properties
Research Applications
  • Birth Outcomes: Specialized support for pregnancy and birth outcome studies
  • Environmental Epidemiology: Designed for environmental health research workflows
  • Risk Assessment: Tools for population-level exposure estimation
  • Policy Research: Support for regulatory and public health decision-making

Known Limitations

  • Geographic Scope: Currently optimized for US-based studies with FIPS codes
  • Data Format Requirements: Specific column naming conventions required for input data
  • Memory Usage: Large datasets may require substantial memory for multiple imputation
  • Convergence: MICE convergence warnings expected for complex imputation models

Future Directions

  • International Support: Expansion to non-US geographic coding systems
  • Additional Contaminants: Framework extension for other groundwater contaminants
  • Advanced Modeling: Integration of spatial autocorrelation and temporal trends
  • Visualization Tools: Enhanced plotting and diagnostic visualization capabilities

Development Team

Principal Investigator: Dr. Matthew O. Gribble (UCSF)
Co-Investigators: Dr. Sayantan Majumdar (DRI), Dr. Ryan G. Smith (Colorado State University)

Funding

This work was supported by the National Heart, Lung, and Blood Institute (R21HL159574) and funding from the United States Geological Survey’s John Wesley Powell Center for Analysis and Synthesis.

Citation

Majumdar, S., Bartell, S. M., Lombard, M. A., Smith, R. G., & Gribble, M. O. (2025). gwArsenicR: An R package for modeling and estimating arsenic exposure from groundwater. Journal of Open Source Software (under review).