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'
URL: https://haijiangqin.com/EFAfactors/
Packaged: 2026-06-24 10:23:12 UTC; root
Config/pak/sysreqs: cmake libgdal-dev gdal-bin libgeos-dev
        libglu1-mesa-dev libgmp3-dev make libgsl0-dev jags libicu-dev
        libpng-dev libuv1-dev libxml2-dev libmpfr-dev libopenmpi-dev
        libssl-dev libproj-dev python3 libx11-dev zlib1g-dev
Repository: https://haijiangq.r-universe.dev
Date/Publication: 2025-10-14 14:40:27 UTC
RemoteUrl: https://github.com/cran/EFAfactors
RemoteRef: HEAD
RemoteSha: d0b8d80fd30aa52dc61e9393f69775229c191b85