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r-efafactors 1.2.3
Propagated dependencies: r-xgboost@1.7.11.1 r-simcormultres@1.9.0 r-reticulate@1.42.0 r-rcpparmadillo@14.4.3-1 r-rcpp@1.0.14 r-ranger@0.17.0 r-psych@2.5.3 r-proxy@0.4-27 r-mlr@2.19.2 r-matrix@1.7-3 r-mass@7.3-65 r-ineq@0.2-13 r-ddpcr@1.15.2 r-checkmate@2.3.2 r-bbmisc@1.13
Channel: guix-cran
Location: guix-cran/packages/e.scm (guix-cran packages e)
Home page: https://haijiangqin.com/EFAfactors/
Licenses: GPL 3
Synopsis: Determining the Number of Factors in Exploratory Factor Analysis
Description:

This package 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.

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