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bmem (version 2.3)

Mediation Analysis with Missing Data Using Bootstrap

Description

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) . Zhang (2014) .

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Version

Install

install.packages('bmem')

Monthly Downloads

369

Version

2.3

License

GPL-2

Maintainer

Zhiyong Zhang

Last Published

August 31st, 2026

Functions in bmem (2.3)

Covariance matrix from EM

Estimate a mediaiton model based on pairwise deletion

Estimate a mediation model based on EM covariance matrix

Estimate a mediaiton model based on listwise deletion

Conducting power analysis based on Sobel test

Conducting power analysis based on bootstrap

Jackknife for pairwise deletion

Calculate bootstrap confidence intervals

Estimate a mediaiton model using SEM technique

Covariance matrix estimation based on pairwise deletion

Convert a raw moment matrix to covariance matrix

Calculate the moments of a data set

Plot of the bootstrap distribution

Get the population parameter values

Bootstrap for listwise deletion method

Covariance matrix for listwise deletion

Organize the results into a table

Select data according to a vector of indices

Jackknife for multiple imputation

Generate a power curve

Obtain missing data pattern information

Bootstrap for EM

Sum square of a matrix

Bootstrap for multiple imputation

Plot of the bootstrap distribution. This function is replaced by plot.

Mediation analysis using sobel test (for complete data only)

Mediation analysis using sobel test for one indirect effect

Covariance estimation for multiple imputation

Mediation analysis based on bootstrap

Calculate the covariance matrix based on a given ram model

Bootstrap but using the Bollen-Stine method

Confidence interval based on normal approximation

Mediation analysis with missing data using bootstrap

Bias-corrected confidence intervals

Percentile confidence interval

Bias-corrected and accelerated confidence intervals

BCa for a single variable

Bias-corrected confidence intervals (for a single variable)

Estimation of robust covariance matrix

Estimate a mediation model based on multiple imputation

Jackknife for listwise deletion

Jackknife estimate using EM

Bootstrap for pairwise deletion