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The mulSEM package provides multivariate analyses using a structural equation modeling (SEM) approach through the OpenMx package. These analyses include canonical correlation analysis (CANCORR), redundancy analysis (RDA), and multivariate principal component regression (MPCR).

You may install it from CRAN by:

install.packages("mulSEM")

The development version can be installed from GitHub:

## Install remotes package if it has not been installed yet
# install.packages("remotes")

remotes::install_github("mikewlcheung/mulsem")

library(mulSEM)

## Canonical Correlation Analysis
cancorr(X_vars=c("Weight", "Waist", "Pulse"),
        Y_vars=c("Chins", "Situps", "Jumps"),
        data=sas_ex1)

## Redundancy Analysis
rda(X_vars=c("x1", "x2", "x3", "x4"),
    Y_vars=c("y1", "y2", "y3"),
    data=sas_ex2)
	
## Multivariate Principal Component Regression	
mpcr(X_vars=c("AU", "CC", "CL", "CO", "DF", "FB", "GR", "MW"),
     Y_vars=c("IDE", "IEE", "IOCB", "IPR", "ITS"),
     pca="COR", pc_select=1,
     data=Nimon21)

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Install

install.packages('mulSEM')

Monthly Downloads

135

Version

1.2

License

GPL (>= 2)

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Maintainer

Mike Cheung

Last Published

February 17th, 2026

Functions in mulSEM (1.2)

cancorr

Canonical correlation analysis
print.MPCR

Print Method for MPCR Objects
mulSEM-package

mulSEM: Some Multivariate Analyses using Structural Equation Modeling
Chittum19

Correlation matrix of a model of motivation
Lambert88

Correlation matrix of artificial data
Nimon21

Raw data used in Nimon, Joo, and Bontrager (2021)
print.RDA

Print Method for RDA Objects
mpcr

Multivariate Principal Component Regression (MPCR)
print.CanCorr

Print Method for CanCorr Objects
sas_ex2

Sample data for redundancy analysis from the SAS manual
rda

Redundancy analysis
Thorndike00

Correlation matrix of a model of disgust
sas_ex1

Sample data for canonical correlation analysis from the SAS manual