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