In confirmatory factor analysis (CFA), structural constraints typically ensure that the model is identified up to all possible reflections, i.e., column sign changes of the matrix of loadings. Such reflection invariance is problematic for Bayesian CFA when the reflection modes are not well separated in the posterior distribution. Imposing rotational constraints -- fixing some loadings to be zero or positive in order to pick a factor solution that corresponds to one reflection mode -- may not provide a satisfactory solution for Bayesian CFA. The function 'relabel' uses the relabeling algorithm of Erosheva and Curtis to correct for sign invariance in MCMC draws from CFA models. The MCMC draws should come from Bayesian CFA models that are fit without rotational constraints.
