SEMdnn                  SEM train with Deep Neural Netwok (DNN) models
SEMml                   Nodewise SEM train using Machine Learning (ML)
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
getLOCO                 Compute variable importance using LOCO values
getShapleyR2            Compute variable importance using Shapley (R2)
                        values
getSignificanceTest     Test for the significance of neural network
                        input nodes
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 DNN
predict.ML              SEM-based out-of-sample prediction using
                        nodewise ML
predict.SEM             SEM-based out-of-sample prediction using
                        layer-wise ordering
trainingReport          Training history evaluation of a DNN model
