Reference

All functions
benchmark()	Prediction benchmark evaluation utility
getConnectionWeight()	Connection Weight Approach for neural network variable importance
getGradientWeight()	Gradient Weight Approach for neural network variable importance
getInputPvalue()	Test for the significance of neural network inputs
getShapleyR2()	Compute variable importance using Shapley (R2) values
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-predictive SEM train using Machine Learning (ML)