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MIRES  

Measurement Invariance Assessment Using Random Effects Models and Shrinkage
View on CRAN: Click here


Download and install MIRES package within the R console
Install from CRAN:
install.packages("MIRES")

Install from Github:
library("remotes")
install_github("cran/MIRES")

Install by package version:
library("remotes")
install_version("MIRES", "0.1.0")



Attach the package and use:
library("MIRES")
Maintained by
Stephen Martin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-22
Latest Update: 2021-02-22
Description:
Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) .
How to cite:
Stephen Martin (2021). MIRES: Measurement Invariance Assessment Using Random Effects Models and Shrinkage. R package version 0.1.0, https://cran.r-project.org/web/packages/MIRES. Accessed 22 Dec. 2024.
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