mma - Multiple Mediation Analysis

Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

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3.96 score 1 stars 1 dependents 61 scripts 348 downloads

mlma - Multilevel Mediation Analysis

Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.

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3.84 score 1 stars 69 scripts 255 downloads

mmabig - Multiple Mediation Analysis for Big Data Sets

Used for general multiple mediation analysis with big data sets.

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2.15 score 14 scripts 181 downloads

bmabart - Bayesian Mediation Analysis Using BART

Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART). The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication.

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2.00 score 2 scripts 152 downloads

BayesianMediationA - Bayesian Mediation Analysis

We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.

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jagscpp

2.00 score 2 scripts 185 downloads

hdbma - Bayesian Mediation Analysis with High-Dimensional Data

Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.

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jagscpp

1.00 score 187 downloads