Reference

All functions
af.softmax()	An Activation Function: Softmax
CD()	the Comparison Data (CD) Approach
CDF()	the Comparison Data Forest (CDF) Approach
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)
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
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
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
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
PA()	Parallel Analysis
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