Version: 0.5.1

Author: Ke-Hai Yuan and Zhiyong Zhang

A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.

• rsem.index: rsem.index function
• semdiag.run.eqs: Run EQS from R
• semdiag.call.eqs:
• rsem.ssq: Calculate the squared sum of a matrix
• rsem.switch: swith function
• rsem.vec: Stacking a matrix to a vector
• rsem.vech: Stacking lower triange of a matrix to a vector
• rsem.se: Calculate robust standard errors
• rsem.emmusig: Robust mean and covariance matrix using Huber-type weight
• rsem.gname: Internal function
• rsem.DP: Generate a duplication matrix
• rsem.lavaan: Conduct robust SEM analysis using lavaan
• rsem: The main function for robust SEM analysis
• rsem.fit: Calculate robust test statistics
• rsem.indexvc: rsem.indexvc function
• rsem.weight: Calculate weight for each subject
• rsem.pattern: Obtaining missing data patterns
• semdiag.combinations: Enumerate the Combinations of the Elements of a Vector
• semdiag.read.eqs: Import of EQS outputs into R
• rsem.print: Organize the output for Lavaan with robust s.e. and test statistics
• rsem.indexv: rsem.indexv function
• rsem.switch.gamma: Internal function
• rsem.Ascov: Sandwich-type covariance matrix