Measurement Invariance Assessment Using Random Effects Models and Shrinkage
Compute BF(Less than)
Unidimensional data generation.
Create Stan-based spike-mixture DP based density estimation function.
Create Stan-based density function.
Create dirichletprocess (exponential) based density function.
Implied density for pairwise differences given HMRE prior.
Density for hmre prior on RE SDs.
Create logspline-based density function.
Combine all unique RHS entries into one RHS formula.
Get the one-length LHS of formula as string.
Get terms from formula list
Get RHS of formula as character vector.
Compute Highest Posterior Density intervals.
Generates indicator spec list.
Outer subtraction for given params across MCMC samples.
Parse formula (list).
Generate labels for all differences of vector.
Compute all differences of vector.
Paper simulation function (For historical purposes)
Stick-breaking function.
The 'MIRES' package.
Fit mixed effects measurement model for invariance assessment.
Pairwise comparisons of random parameters.
Create marginal posterior density function approximations for random e...
Prediction for DP density estimation models.
Print function for mires objects.
Print method for MIRES summary objects.
Extract random effects of each group from MIRES model.
Random sampling from hmre prior on RE SDs.
Generate Truncated Dirichlet Process Mixture.
Split stan names into a list of parameter names (char vec) and (col-na...
Summary method for mires object.
Tidy up a vector of stan names into a data frame.
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) <doi:10.31234/osf.io/qbdjt>.