Mediation Analysis with Missing Data Using Bootstrap

Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.

Mediation Analysis with Missing Data and Non-Normal Data
c(
person("Shuigen", "Ming", role = "aut", email = "msg0@mail.ustc.edu.cn"),
person("Hong", "Zhang", role = "aut", email = "zhangh@ustc.edu.cn"),
person("Zhiyong", "Zhang", role = c("aut", "cre"), email = "johnnyzhz@gmail.com"),
person("Lijuan", "Wang", role = "aut", email = "lwang4@nd.edu"))
R (>= 3.0), Amelia, MASS, snowfall, rsem
Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).
Shuigen Ming [aut],
Hong Zhang [aut],
Zhiyong Zhang [aut, cre],
Lijuan Wang [aut]

R (>= 3.5.0), Amelia, MASS, snowfall, rsem

A Package for Bayesian Meta-Analysis and Meta-Regression
Provides a collection of functions for conducting meta-analyses under Bayesian context in R. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data based on MCMC simulations. Users are allowed to fit fixed- and random-effects models with different priors to the data. Meta-regression can be carried out if effects of additional covariates are observed. Furthermore, the package provides functions for creating posterior distribution plots and forest plot to display main model output. Traceplots and some other diagnostic plots are also available for assessing model fit and performance.