CD                      the Comparison Data (CD) Approach
CDF                     the Comparison Data Forest (CDF) Approach
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
MAP                     Minimum Average Partial (MAP) Test
NN                      the pre-trained Neural Networks for Determining
                        the Number of Factors
PA                      Parallel Analysis
STOC                    Scree Test Optimal Coordinate (STOC)
af.softmax              An Activation Function: Softmax
check_python_libraries
                        Check and Install Python Libraries (numpy and
                        onnxruntime)
data.DAPCS              20-item Dependency-Oriented and
                        Achievement-Oriented Psychological Control
                        Scale (DAPCS)
data.bfi                25 Personality Items Representing 5 Factors
data.datasets.DNN       Subset Dataset for Training the Deep Neural
                        Network (DNN)
data.datasets.LSTM      Subset Dataset for Training the Long Short Term
                        Memory (LSTM) Network
data.scaler.DNN         the Scaler for the pre-trained Deep Neural
                        Network (DNN)
data.scaler.LSTM        the Scaler for the pre-trained Long Short Term
                        Memory (LSTM) Network
extractor.feature.FF    Extracting features According to Goretzko &
                        Buhner (2020)
extractor.feature.NN    Extracting features for the pre-trained Neural
                        Networks for Determining the Number of Factors
factor.analysis         Factor Analysis by Principal Axis Factoring
load.NN                 Load the the pre-trained Neural Networks for
                        Determining the Number of Factors
load.scaler             Load the Scaler for the pre-trained Neural
                        Networks for Determining the Number of Factors
load.xgb                Load the Tuned XGBoost Model
model.xgb               the Tuned XGBoost Model for Determining the
                        Number of Facotrs
normalizor              Feature Normalization for the pre-trained
                        Neural Networks for Determining the Number of
                        Factors
plot                    Plot Methods
predictLearner.classif.xgboost.earlystop
                        Prediction Function for the Tuned XGBoost Model
                        with Early Stopping
print                   Print Methods
