CD                      the Comparison Data (CD) Approach
CDF                     the Comparison Data Forest (CDF) Approach
DNN_predictor           A Pre-Trained Deep Neural Network (DNN) for
                        Determining the Number of Factors
EFAhclust               Hierarchical Clustering for EFA
EFAindex                Various Indeces in EFA
EFAkmeans               K-means for EFA
EFAscreet               Scree Plot
EFAsim.data             Simulate Data that Conforms to the theory of
                        Exploratory Factor Analysis.
EFAvote                 Voting Method for Number of Factors in EFA
EKC                     Empirical Kaiser Criterion
FF                      Factor Forest (FF) Powered by An Tuned XGBoost
                        Model for Determining the Number of Factors
GenData                 Simulating Data Following John Ruscio's
                        RGenData
Hull                    the Hull Approach
KGC                     Kaiser-Guttman Criterion
PA                      Parallel Analysis
af.softmax              An Activation Function: Softmax
check_python_libraries
                        Check and Install Python Libraries (numpy and
                        onnxruntime)
data.bfi                25 Personality Items Representing 5 Factors
data.datasets           Subset Dataset for Training the Pre-Trained
                        Deep Neural Network (DNN)
data.scaler             the Scaler for the Pre-Trained Deep Neural
                        Network (DNN)
extractor.feature.DNN   Extracting features for the Pre-Trained Deep
                        Neural Network (DNN)
extractor.feature.FF    Extracting features According to Goretzko &
                        Buhner (2020)
factor.analysis         Factor Analysis by Principal Axis Factoring
load_DNN                Load the Trained Deep Neural Network (DNN)
load_scaler             Load the Scaler for the Pre-Trained Deep Neural
                        Network (DNN)
load_xgb                Load the Tuned XGBoost Model
model.xgb               the Tuned XGBoost Model for Determining the
                        Number of Facotrs
normalizor              Feature Normalization
plot.CD                 Plot Comparison Data for Factor Analysis
plot.CDF                Plot Comparison Data Forest (CDF)
                        Classification Probability Distribution
plot.DNN_predictor      Plot DNN Predictor Classification Probability
                        Distribution
plot.EFAhclust          Plot Hierarchical Cluster Analysis Dendrogram
plot.EFAkmeans          Plot EFA K-means Clustering Results
plot.EFAscreet          Plots the Scree Plot
plot.EFAvote            Plot Voting Results for Number of Factors
plot.EKC                Plot Empirical Kaiser Criterion (EKC) Plot
plot.FF                 Plot Factor Forest (FF) Classification
                        Probability Distribution
plot.Hull               Plot Hull Plot for Factor Analysis
plot.KGC                Plot Kaiser-Guttman Criterion (KGC) Plot
plot.PA                 Plot Parallel Analysis Scree Plot
predictLearner.classif.xgboost.earlystop
                        Prediction Function for the Tuned XGBoost Model
                        with Early Stopping
print.CD                Print Comparison Data Method Results
print.CDF               Print Comparison Data Forest (CDF) Results
print.DNN_predictor     Print DNN Predictor Method Results
print.EFAdata           Print the EFAsim.data
print.EFAhclust         Print EFAhclust Method Results
print.EFAkmeans         Print EFAkmeans Method Results
print.EFAscreet         Print the Scree Plot
print.EFAvote           Print Voting Method Results
print.EKC               Print Empirical Kaiser Criterion Results
print.FF                Print Factor Forest (FF) Results
print.Hull              Print Hull Method Results
print.KGC               Print Kaiser-Guttman Criterion Results
print.PA                Print Parallel Analysis Method Results
