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rsem (version 0.5.0)

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

Description

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.

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Version

Install

install.packages('rsem')

Monthly Downloads

493

Version

0.5.0

License

GPL-2

Maintainer

Zhiyong Zhang

Last Published

April 21st, 2020

Functions in rsem (0.5.0)

rsem.switch

swith function
rsem.switch.gamma

Internal function
semdiag.run.eqs

Run EQS from R
rsem.vech

Stacking lower triange of a matrix to a vector
rsem.vec

Stacking a matrix to a vector
semdiag.read.eqs

Import of EQS outputs into R
rsem.se

Calculate robust standard errors
rsem.ssq

Calculate the squared sum of a matrix
rsem.emmusig

Robust mean and covariance matrix using Huber-type weight
rsem.indexvc

rsem.indexvc function
rsem.lavaan

Conduct robust SEM analysis using lavaan
rsem.weight

Calculate weight for each subject
rsem.pattern

Obtaining missing data patterns
semdiag.combinations

Enumerate the Combinations of the Elements of a Vector
rsem.print

Organize the output for Lavaan with robust s.e. and test statistics
mardiamv25

Simulated data
rsem.fit

Calculate robust test statistics
rsem.indexv

rsem.indexv function
rsem.index

rsem.index function
rsem

The main function for robust SEM analysis
rsem.DP

Generate a duplication matrix
rsem.gname

Internal function
rsem.Ascov

Sandwich-type covariance matrix
rsem-package

Robust Structural Equation Modeling with Missing Data and Auxiliary