Package: mulSEM 1.2

mulSEM: Some Multivariate Analyses using Structural Equation Modeling

A set of functions for some multivariate analyses utilizing 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). It implements procedures discussed in Gu and Cheung (2023) <doi:10.1111/bmsp.12301>, Gu, Yung, and Cheung (2019) <doi:10.1080/00273171.2018.1512847>, and Gu et al. (2023) <doi:10.1080/00273171.2022.2141675>.

Authors:Mike Cheung [aut, cre], Fei Gu [ctb], Yiu-Fai Yung [ctb]

mulSEM_1.2.tar.gz
mulSEM_1.2.zip(r-4.7-any)mulSEM_1.2.zip(r-4.6-any)mulSEM_1.2.zip(r-4.5-any)
mulSEM_1.2.tgz(r-4.6-any)mulSEM_1.2.tgz(r-4.5-any)
mulSEM_1.2.tar.gz(r-4.7-any)mulSEM_1.2.tar.gz(r-4.6-any)
mulSEM_1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
mulSEM/json (API)

# Install 'mulSEM' in R:
install.packages('mulSEM', repos = c('https://mikewlcheung.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mikewlcheung/mulsem/issues

Datasets:
  • Chittum19 - Correlation matrix of a model of motivation
  • Lambert88 - Correlation matrix of artificial data
  • Nimon21 - Raw data used in Nimon, Joo, and Bontrager
  • sas_ex1 - Sample data for canonical correlation analysis from the SAS manual
  • sas_ex2 - Sample data for redundancy analysis from the SAS manual
  • Thorndike00 - Correlation matrix of a model of disgust

On CRAN:

Conda:

3.00 score 1 stars 1 scripts 178 downloads 3 exports 15 dependencies

Last updated from:505d5367ef. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK291
source / vignettesOK215
linux-release-x86_64OK312
macos-release-arm64OK94
macos-oldrel-arm64OK118
windows-develOK101
windows-releaseOK117
windows-oldrelOK121
wasm-releaseOK113

Exports:cancorrmpcrrda

Dependencies:BHclidigestlatticelifecycleMASSMatrixmvtnormOpenMxRcppRcppEigenRcppParallelrlangrpfStanHeaders