mediationverse

Unified ecosystem for mediation analysis in R

Overview

The mediationverse is a collection of R packages for mediation analysis, providing a unified ecosystem for:

• 🔧 Model fitting and extraction
• 📊 Effect size computation
• 📈 Confidence interval estimation
• 🛡️ Sensitivity analysis
• 🔬 Simulation studies

Quick Start

# Install mediationverse
pak::pak("data-wise/mediationverse")

# Load all packages at once
library(mediationverse)

# Fit mediation models
fit_m <- lm(M ~ X + C, data = mydata)
fit_y <- lm(Y ~ X + M + C, data = mydata)
med_data <- extract_mediation(fit_m, model_y = fit_y,
                               treatment = "X", mediator = "M")

# Get P_med effect size, confidence intervals, and sensitivity analysis
pmed_result <- compute_pmed(med_data)
ci_result <- ci(med_data, type = "dop")
sensitivity <- sensitivity_analysis(med_data)

Ecosystem Status Dashboard

📊 View Full Dashboard | 🗺️ Development Roadmap

Package Overview

Package	Status	Build	Website	Role
medfit				Foundation
probmed				Effect Size
RMediation				Confidence Intervals
medrobust				Sensitivity Analysis
medsim				Simulation Infrastructure

Development Progress

Current Quarter (Q4 2025): Ecosystem Development Phase

Milestone	Status	Progress
medfit Phase 3 (Extraction)	✅ Complete	100%
medfit Phase 4 (Model Fitting)	🚧 In Progress	60%
probmed Integration	⏳ Pending	0%
RMediation Integration	⏳ Pending	0%
medsim Core Implementation	✅ Complete	100%
Comprehensive Documentation	✅ Complete	100%

Next Quarter (Q1 2026): Integration & Release

• Complete medfit Phase 4-5 (Bootstrap, Testing)
• Integrate probmed and RMediation with medfit
• CRAN submissions for medfit, probmed
• Finalize mediationverse meta-package

Package Ecosystem

The mediationverse follows a modular architecture inspired by tidyverse and easystats:

                    ┌─────────────┐
                    │   medfit    │
                    │ (Foundation)│
                    └──────┬──────┘
                           │
            ┌──────────────┼──────────────┐
            │              │              │
      ┌─────▼─────┐  ┌────▼────┐  ┌──────▼──────┐
      │  probmed  │  │RMediation│  │  medrobust  │
      │   (P_med) │  │(DOP,MBCO)│  │(Sensitivity)│
      └───────────┘  └─────────┘  └─────────────┘
                           │
                    ┌──────▼──────┐
                    │   medsim    │
                    │ (Simulation)│
                    └─────────────┘

Core Packages

medfit: Foundation Infrastructure

Purpose: Unified S7-based infrastructure for mediation analysis Status: Phase 4 in progress Provides:

• S7 classes (MediationData, SerialMediationData, BootstrapResult)
• Model extraction from lm/glm/lavaan
• Bootstrap inference (parametric, nonparametric, plugin)
• Type-safe data structures

Links: Website | GitHub | Issues

probmed: Probabilistic Effect Sizes

Purpose: Compute P_med, a scale-free probabilistic effect size Status: Stable, ready for integration Provides:

• P_med computation for continuous and binary outcomes
• GLM support (logistic, Poisson)
• Integration with lavaan and mediation packages
• Bootstrap confidence intervals

Links: Website | GitHub | Issues

RMediation: Confidence Intervals

Purpose: Asymmetric confidence intervals via Distribution of Product Status: Stable on CRAN Provides:

• Distribution of Product (DOP) method
• Monte Carlo (MC) confidence intervals
• MBCO bootstrap method
• Integration with standard R models

Links: CRAN | Website | GitHub

medrobust: Sensitivity Analysis

Purpose: Partial identification under differential misclassification Status: In development Provides:

• Partial identification bounds for NDE/NIE
• Data-driven falsification tests
• Synthetic data generation
• Publication-ready visualizations

Links: Website | GitHub | Issues

medsim: Simulation Infrastructure

Purpose: Standardized infrastructure for Monte Carlo studies Status: Core implementation complete Provides:

• Environment-aware execution (local/HPC)
• Parallel processing with progress bars
• Ground truth caching
• Publication-ready figures and LaTeX tables

Links: Website | GitHub | Issues

Installation

From GitHub (Development)

# Install all packages
pak::pak("Data-Wise/mediationverse")

# Or install individually
pak::pak("Data-Wise/medfit")
pak::pak("Data-Wise/probmed")
pak::pak("Data-Wise/medrobust")
pak::pak("Data-Wise/medsim")

# RMediation from CRAN
install.packages("RMediation")

From CRAN (Future)

# Planned for Q2-Q3 2026
install.packages("mediationverse")

Usage

Loading Packages

library(mediationverse)
#> ── Attaching packages ─────────────────────────────── mediationverse 0.0.0.9000 ──
#> ✔ medfit     0.1.0     ✔ probmed    0.1.0
#> ✔ RMediation 1.4.0     ✔ medrobust  0.1.0
#> ✔ medsim     0.1.0
#> ──────────────────────────────────────────────────────────────────────────────────

Package Management

# List installed packages and versions
mediationverse_packages()

# Update all packages
mediationverse_update()

# Check for function conflicts
mediationverse_conflicts()

Complete Analysis Workflow

library(mediationverse)

# 1. Fit mediation models (medfit)
fit_m <- lm(M ~ X + C, data = mydata)
fit_y <- lm(Y ~ X + M + C, data = mydata)

# 2. Extract mediation structure (medfit)
med_data <- extract_mediation(fit_m, model_y = fit_y,
                               treatment = "X", mediator = "M")

# 3. Bootstrap inference (medfit)
boot_result <- bootstrap_mediation(med_data, n_boot = 2000)

# 4. Compute probabilistic effect size (probmed)
pmed_result <- compute_pmed(med_data)

# 5. Get confidence intervals (RMediation)
ci_result <- ci(med_data, type = "dop")

# 6. Sensitivity analysis (medrobust)
robust_result <- sensitivity_analysis(med_data)

# 7. Run simulation study (medsim)
sim_results <- medsim_run(
  method = my_method,
  scenarios = medsim_scenarios_mediation(),
  config = medsim_config("local")
)

Design Philosophy

Following principles from tidyverse and easystats:

1. Foundation Package: medfit provides shared S7 infrastructure
2. Specialized Packages: Each package focuses on one methodological contribution
3. Consistent API: Unified interfaces across all packages
4. Type Safety: S7 classes ensure data integrity
5. Low Dependencies: Minimal external dependencies for stability
6. Comprehensive Testing: >90% code coverage across packages

Documentation

Getting Started

• Quick Start Guide
• Complete Workflow

Status & Planning

• 📊 Package Status Dashboard
• 🗺️ Development Roadmap
• 🤝 Contributing Guide
• 📋 Ecosystem Coordination

Package Documentation

• medfit Documentation
• probmed Documentation
• RMediation Documentation
• medrobust Documentation
• medsim Documentation

Contributing

The mediationverse is in active development. Contributions are welcome!

• 🐛 Report bugs: GitHub Issues
• 💡 Suggest features: Discussions
• 🔧 Contribute code: See our Contributing Guide
• 📖 Improve docs: Submit pull requests

Development Workflow:

1. Fork the repository
2. Create a feature branch from dev
3. Make your changes with tests
4. Submit a pull request to dev

See CONTRIBUTING.md for detailed guidelines.

Citation

If you use packages from the mediationverse in your research, please cite the individual packages:

Tofighi, D. (2025). medfit: Infrastructure for mediation analysis in R.
R package version 0.1.0. https://github.com/data-wise/medfit

Tofighi, D. (2025). probmed: Probabilistic effect sizes for mediation analysis.
R package version 0.1.0. https://github.com/data-wise/probmed

Full citations available via citation("packagename").

Related Ecosystems

The mediationverse draws inspiration from:

• tidyverse - Cohesive data science packages
• easystats - Statistical modeling ecosystem
• mlr3 - Machine learning framework

Code of Conduct

Please note that the mediationverse project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

GPL (>= 3)

Contact

Maintainer: Davood Tofighi (dtofighi@gmail.com) ORCID: 0000-0001-8523-7776

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Sources:

• tidyverse meta-package structure
• easystats ecosystem documentation