Provides a collection of standard factor retention methods in Exploratory Factor 
             Analysis (EFA), making it easier to determine the number of factors. Traditional 
             methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, 
             Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and 
             Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis 
             (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA 
             are readily available. This package also implements several newer methods, such as 
             the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) 
             <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) 
             <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) 
             <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like 
             Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) 
             <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner 
             (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network 
             (DNN) trained on large-scale datasets that can efficiently and reliably determine 
             the number of factors.

Provides a complete workflow for exploratory factor analysis
    (EFA). It covers data screening and factorability checks, a suite of
    factor retention criteria for choosing the number of factors, and factor
    extraction by principal axis factoring, maximum likelihood, unweighted
    least squares, or diagonally weighted least squares from Pearson,
    Spearman, Kendall, polychoric, tetrachoric, or two-stage
    full-information maximum likelihood correlations. A built-in rotation
    engine offers a range of orthogonal and oblique rotations, and standard
    errors for loadings and related quantities can be obtained by analytic,
    robust, or bootstrap methods. Further tools support model averaging
    across analytic choices, multigroup EFA with factor congruence, EFA on
    multiply imputed data, Schmid-Leiman transformation, reliability
    coefficients (including McDonald's omegas), factor score estimation,
    data simulation, and power analysis. Computationally intensive
    procedures are implemented in 'C++' for speed.