Package: EFAfactors
Type: Package
Title: Determining the Number of Factors in Exploratory Factor Analysis
Version: 1.2.4
Date: 2025-10-13
Author: 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>)
Authors@R: c(person(given = "Haijiang", 
                    family = "Qin", 
                    role = c("aut", "cre", "cph"), 
                    email = "haijiang133@outlook.com", 
                    comment = c(ORCID = "0009-0000-6721-5653")),
             person(given = "Lei", 
                    family = "Guo", 
                    role = c("aut", "cph"), 
                    email = "happygl1229@swu.edu.cn", 
                    comment = c(ORCID = "0000-0002-8273-3587")))
Maintainer: Haijiang Qin <haijiang133@outlook.com>
Description: 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.
License: GPL-3
Depends: R (>= 4.3.0)
Imports: BBmisc, checkmate, ddpcr, ineq, MASS, Matrix, mlr, proxy,
        psych, ranger, reticulate, Rcpp, RcppArmadillo, SimCorMultRes,
        xgboost
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.3.2
Encoding: UTF-8
NeedsCompilation: yes
Collate: 'CD.R' 'CDF.R' 'check_python_libraries.R' 'data.bfi.R'
        'data.DAPCS.R' 'data.datasets.DNN.R' 'data.datasets.LSTM.R'
        'data.scaler.DNN.R' 'data.scaler.LSTM.R' 'NN.R' 'EFAhclust.R'
        'EFAindex.R' 'EFAkmeans.R' 'EFAvote.R' 'EKC.R' 'EFAscreet.R'
        'EFAsim.data.R' 'extractor.feature.NN.R'
        'extractor.feature.FF.R' 'factor.analysis.R' 'FF.R' 'GenData.R'
        'get.runs.R' 'Hull.R' 'KGC.R' 'load.R' 'MAP.R' 'model.xgb.R'
        'normalizor.R' 'PA.R' 'ParamHelpers.R' 'plot.R' 'print.R'
        'RcppExports.R' 'af.softmax.R' 'utils.R' 'zzz.R' 'STOC.R'
Repository: CRAN
URL: https://haijiangqin.com/EFAfactors/
Packaged: 2025-10-14 13:20:55 UTC; Haijiang
Date/Publication: 2025-10-14 14:00:09 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-05-20 04:10:26 UTC; windows
Archs: x64
