Reference

All functions
classificationReport()	Prediction evaluation report of a classification model
crossValidation()	Cross-validation of linear SEM, ML or DNN training models
getConnectionWeight()	Connection Weight method for neural network variable importance
getGradientWeight()	Gradient Weight method for neural network variable importance
getShapleyR2()	Compute variable importance using Shapley (R2) values
getSignificanceTest()	Test for the significance of neural network inputs
getVariableImportance()	Variable importance for Machine Learning models
mapGraph()	Map additional variables (nodes) to a graph object
nplot()	Create a plot for a neural network model
predict(<DNN>)	SEM-based out-of-sample prediction using layer-wise DNN
predict(<ML>)	SEM-based out-of-sample prediction using node-wise ML
predict(<SEM>)	SEM-based out-of-sample prediction using layer-wise ordering
SEMdnn()	Layer-wise SEM train with a Deep Neural Netwok (DNN)
SEMml()	Nodewise SEM train using Machine Learning (ML)