SEMdeep0.1.0 package

Structural Equation Modeling with Deep Neural Network and Machine Learning

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)

Download source packageRead PDF manual

Training and validation of a custom (or data-driven) Structural Equation Models using layer-wise Deep Neural Networks or node-wise Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.