Determining the Number of Factors in Exploratory Factor Analysis R package EFAfactors version 1.2.4 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. Software Depends: R (>= 4.3.0) Imports: BBmisc, checkmate, ddpcr, ineq, MASS, Matrix, mlr, proxy, psych, ranger, reticulate, Rcpp, RcppArmadillo, SimCorMultRes, xgboost LinkingTo: Rcpp, RcppArmadillo Haijiang Qin <haijiang133@outlook.com> Comprehensive R Archive Network (CRAN) Haijiang Qin [aut, cre, cph] (ORCID: <https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph] (ORCID: <https://orcid.org/0000-0002-8273-3587>) GPL-3 2025-10-14 application/tgz https://CRAN.R-project.org/package=EFAfactors doi:10.32614/CRAN.package.EFAfactors