Fit Latent Multivariate Mixed Effects Location Scale Models
Extract group-specific coefficients.
Adds group codings for predictive df.
Combines multiple formulas' RHS into one.
Detect whether the predictors are L2-only
Get indices for subsetting lower-tri summaries of square matrices.
Gets elapsed time.
Get names in formula.
Get indicator spec for stan model.
Get LHS variable as string.
Get RHS terms or variables.
Zip two lists together with function.
Convert char vector to columns.
Print newline.
Convert stan par-string to numeric columns.
Compute indicator data.
Compute predictor data.
Convert spec to stan data.
Print separator.
Simulate covariates without correlation.
Compute posterior summaries.
Rearrange summary output.
Takes stan summary, returns summary with indices-as-columns.
Check for location-scale formulas
Extracted model fitted variates.
Operator for testing NULL and returning expr if NULL
The 'LMMELSM' package.
Specify and fit the (latent) (multivariate) melsm.
loo method for LMMELSM objects.
Creates named list.
Predict method for lmmelsm objects.
Print method for lmmelsm objects.
Print method for summary.lmmelsm objects.
Extract random effects.
Multiply a row by a list of matrices
Simulate data from latent uni/multidimensional MELSM
Summary method for lmmelsm objects.
In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.