Reference

All functions

af.softmax()

An Activation Function: Softmax

CD()

the Comparison Data (CD) Approach

CDF()

the Comparison Data Forest (CDF) Approach

check_python_libraries()

Check and Install Python Libraries (numpy and onnxruntime)

data.bfi

25 Personality Items Representing 5 Factors

data.DAPCS

20-item Dependency-Oriented and Achievement-Oriented Psychological Control Scale (DAPCS)

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

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.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

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.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

MAP()

Minimum Average Partial (MAP) Test

model.xgb

the Tuned XGBoost Model for Determining the Number of Facotrs

NN()

the pre-trained Neural Networks for Determining the Number of Factors

normalizor()

Feature Normalization for the pre-trained Neural Networks for Determining the Number of Factors

PA()

Parallel Analysis

plot(<Hull>) plot(<CD>) plot(<PA>) plot(<EKC>) plot(<KGC>) plot(<EFAkmeans>) plot(<EFAhclust>) plot(<NN>) plot(<FF>) plot(<CDF>) plot(<EFAvote>) plot(<EFAscreet>) plot(<MAP>) plot(<STOC>)

Plot Methods

predictLearner(<classif.xgboost.earlystop>)

Prediction Function for the Tuned XGBoost Model with Early Stopping

print(<Hull>) print(<CD>) print(<PA>) print(<EKC>) print(<KGC>) print(<EFAhclust>) print(<NN>) print(<FF>) print(<CDF>) print(<EFAvote>) print(<EFAdata>) print(<EFAscreet>) print(<MAP>)

Print Methods

STOC()

Scree Test Optimal Coordinate (STOC)