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  "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:\n<https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph]\n(ORCID: <https://orcid.org/0000-0002-8273-3587>)",
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  "Description": "Provides a collection of standard factor retention methods\nin Exploratory Factor Analysis (EFA), making it easier to\ndetermine the number of factors. Traditional methods such as\nthe scree plot by Cattell (1966)\n<doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion\n(KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser\n(1960) <doi:10.1177/001316446002000116>, and flexible Parallel\nAnalysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on\neigenvalues form PCA or EFA are readily available. This package\nalso implements several newer methods, such as the Empirical\nKaiser Criterion (EKC) by Braeken and van Assen (2017)\n<doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and\nRoche (2012) <doi:10.1037/a0025697>, and Hull method by\nLorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>,\nas well as some AI-based methods like Comparison Data Forest\n(CDF) by Goretzko and Ruscio (2024)\n<doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by\nGoretzko and Buhner (2020) <doi:10.1037/met0000262>.\nAdditionally, it includes a deep neural network (DNN) trained\non large-scale datasets that can efficiently and reliably\ndetermine the number of factors.",
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