This function quantifies and assesses the consequences of parcel-allocation variability for model ranking of structural equation models (SEMs) that differ in their structural specification but share the same parcel-level measurement specification (see Sterba & Rights, 2016). This function is a modified version of parcelAllocation which can be used with only one SEM in isolation. The PAVranking function repeatedly generates a specified number of random item-to-parcel allocations, and then fits two models to each allocation. Output includes summary information about the distribution of model selection results (including plots) and the distribution of results for each model individually, across allocations within-sample. Note that this function can be used when selecting among more than two competing structural models as well (see instructions below involving seed).
lavaanStar-class
(Package: semTools) :
Class For Representing A (Fitted) Latent Variable Model with Additional Elements
This is the lavaan class that contains additional information about the fit values from the null model. Some functions are adjusted according to the change.
Probing interaction for simple intercept and simple slope for the residual-centered latent two-way interaction (Pornprasertmanit, Schoemann, Geldhof, & Little, submitted)
Analyzing data with full-information maximum likelihood with auxiliary variables. The techniques used to account for auxiliary variables are both extra-dependent-variables and saturated-correlates approaches (Enders, 2008). The extra-dependent-variables approach is used for all single variables in the model (such as covariates or single-indicator dependent varaible) For variables that are belong to a multiple-indicator factor, the saturated-correlates approach is used. Note that all covariates are treated as endogenous varaibles in this model (fixed.x = FALSE) so multivariate normality is assumed for the covariates. CAUTION: (1) this function will automatically change the missing data handling method to full-information maximum likelihood and (2) this function is still not applicable for categorical variables (because the maximum likelhood method is not available in lavaan for estimating models with categorical variables currently).
This function takes data with missing observations, multiple imputes the data, runs a SEM using lavaan and combines the results using Rubin's rules. Note that parameter estimates and standard errors are pooled by the Rubin's (1987) rule. The chi-square statistics and the related fit indices are pooled by the method described in "chi" argument. SRMR is calculated based on the average model-implied means and covariance matrices across imputations.
Analyzing data using a null model by full-information maximum likelihood. In the null model, all means and covariances are free if items are continuous. All covariances are fixed to 0. For ordinal variables, their means are fixed as 0 and their variances are fixed as 1 where their thresholds are estimated. In multiple-group model, all means are variances are separately estimated.
Analyzing data using a saturate model by full-information maximum likelihood. In the saturate model, all means and covariances are free if items are continuous. For ordinal variables, their means are fixed as 0 and their variances are fixed as 1–their covariances and thresholds are estimated. In multiple-group model, all means are variances are separately estimated.
providing 
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