Measurement Invariance Assessment Using Random Effects Models and Shrinkage

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) .


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install.packages("MIRES")

0.1.0 by Stephen Martin, 11 days ago


Report a bug at https://github.com/stephenSRMMartin/MIRES/issues


Browse source code at https://github.com/cran/MIRES


Authors: Stephen Martin [aut, cre] , Philippe Rast [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports methods, Rcpp, rstan, rstantools, Formula, stats, parallel, mvtnorm, dirichletprocess, truncnorm, pracma, cubature, logspline, nlme, HDInterval

Suggests testthat

Linking to BH, Rcpp, RcppEigen, rstan, StanHeaders

System requirements: GNU make


See at CRAN