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 functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.

A number of utility function for exploratory factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.