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RMediation - Mediation Analysis Confidence Intervals

Computes confidence intervals for nonlinear functions of model parameters (e.g., product of k coefficients) in single-level and multilevel structural equation models. Methods include the distribution of the product, Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based Constrained Optimization (MBCO) procedure for hypothesis testing of indirect effects. References: Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692-700. <doi:10.3758/s13428-011-0076-x>; Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25(4), 496-515. <doi:10.1037/met0000259>; Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.

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causal-inferenceconfidence-intervalslikelihood-ratio-testmediationmediation-analysis

7.59 score 2 stars 1 dependents 62 scripts 585 downloads

medfit - Infrastructure for Mediation Model Fitting and Extraction

Provides S7-based infrastructure for fitting mediation models, extracting path coefficients, and performing bootstrap inference. Designed as a foundation package for the mediation analysis ecosystem, supporting 'probmed', 'RMediation', and 'medrobust' packages. Implements unified interfaces for model fitting across different engines (generalized linear models, linear mixed models for cluster-randomized designs, and an experimental maximum-likelihood structural equation engine; Bayesian methods are planned), standardized extraction of mediation paths from various model types, and robust bootstrap inference methods. Mediation methods are described in MacKinnon, Lockwood and Williams (2004) <doi:10.1207/s15327906mbr3901_4>, Preacher and Hayes (2008) <doi:10.3758/brm.40.3.879>, Tofighi and MacKinnon (2011) <doi:10.3758/s13428-011-0076-x>, and VanderWeele (2014) <doi:10.1097/EDE.0000000000000121>.

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5.20 score 1 stars 3 dependents 39 scripts 270 downloads

medsim - Simulation Infrastructure for Mediation Analysis

Provides standardized infrastructure for conducting Monte Carlo simulation studies in mediation analysis. Features include environment-aware execution (local vs HPC cluster), parallel processing with progress reporting, ground truth caching, automated result analysis, and publication-ready visualization. Designed to work seamlessly with the mediationverse ecosystem: 'medfit', 'probmed', 'RMediation', and 'medrobust'.

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quarto

4.56 score 18 scripts

probmed - Probabilistic Effect Sizes for Causal Mediation Analysis

Compute P_med, a scale-free probabilistic effect size for causal mediation analysis.

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quarto

4.41 score 1 stars 43 scripts

missingmed - Mediation Analysis with Multiple Imputation for Missing Data

Mediation analysis with incomplete data. Fits the mediator and outcome regressions on each dataset imputed with 'mice' (through 'medfit'), pools them with Rubin's rules, and infers the indirect effect with a Monte Carlo confidence interval ('RMediation') or a D4-stacked model-based constrained optimization (MBCO) likelihood-ratio test. Also provides an inverse probability weighting estimator, treatment-by-mediator interaction models, and a delta-adjustment sensitivity analysis for departures from missing at random. Structural equation models, including a latent mediator, are fit with 'lavaan'.

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4.40 score 21 scripts

mediationverse - Ecosystem for Mediation Analysis in R

A meta-package that loads the core packages in the mediation analysis ecosystem: 'medfit' (infrastructure) and 'RMediation' (confidence intervals). This package simplifies installation and provides a unified entry point for mediation analysis in R. Inspired by the 'tidyverse' and 'easystats' ecosystems.

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causal-inferencecausal-modelsmediation-analysissensitivity-analysisquarto

4.36 score 1 stars 22 scripts

medrobust - Robust Causal Mediation Analysis Under Differential Misclassification

Provides tools for conducting sensitivity analysis for causal mediation effects when the exposure or mediator is measured with differential misclassification (e.g., recall bias, outcome-dependent measurement error). Unlike existing measurement error correction methods that assume non-differential error or require validation data, 'medrobust' derives partial identification bounds that remain valid without gold-standard measurements. The package implements methods developed in Tofighi (2025) for partial identification bounds for Natural Direct Effects (NDE) and Natural Indirect Effects (NIE), data-driven falsification via testable implications, sensitivity analysis over user-specified ranges of misclassification parameters, diagnostic tools and publication-quality visualizations, bootstrap inference for confidence intervals via percentile and bias-corrected and accelerated (BCa) methods, and synthetic data generation for power analysis and methods research. The package handles both mediator misclassification and exposure misclassification within a unified framework. The partial identification approach builds on Manski (2003, ISBN:978-0387004549); confidence intervals for the partially identified bounds use the construction of Imbens and Manski (2004) <doi:10.1111/j.1468-0262.2004.00549.x>.

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causal-inferencecausal-mediationmeasurementmeasurement-errormediation-analysis

2.92 score 21 scripts