MIIVefa is data-driven algorithm for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.
Package and algorithm created by Ken Bollen, Katie Gates, & Lan Luo.
https://cran.r-project.org/web/packages/MIIVefa/vignettes/my-vignette.html
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