Version 1.1.1 Release Notes

• Bugs in the SEMml() function were fixed to address the update of xboost v3.1.2.1.

• Added new function trainingReport(). Display a (r,c) panel history plot from SEMdnn() output, for each MLP model (if algo=“nodewise or”layerwise”) and bootstrap sample (if nboot > 0), in order to check the model’s learning process.

Version 1.1.0 Release Notes

• All new and revised torch functions no longer require the “cito” package.

• Added new argument algo = c(“nodewise”,“layerwise”,“structured”,“neuralgraph”) to the SEMdnn() function. Four algorithms are now implemented using R MLPs (number of nodes with non-zero incoming connectivity) for “nodewise”, L<R MLPs (number of layers in the input graph) for “layerwise”, and 1 MLP for “structured” or “neuralgraph”.

• Delete algo=“nn” and algo=“dnn” in the SEMml() function. These NNs for a small neural network model (1 hidden layer and 10 nodes) and large neural network model (1 hidden layers and 1000 nodes) run with algo=“nodewise” of the SEMdnn() function.

• Added new function getLOCO(). For SEMml() algo=c(“sem”,“tree”,“rf”,“xgb”), computes the contributions of each variable to individual predictions using LOCO (Leave Out COvariates) values based on ghost variables. A CPU-efficient procedure.

• Various fixed bugs discovered after the release 1.0.0.

Version 1.0.0 Release Notes

• Version 1.0.0 is a major release with several new features, including:

• Added new argument outcome = NULL (defult). This parameter is used in SEMdnn() and SEMml() functions to process a sink categorical node (as a factor) for classification purposes using all graph nodes as covariates.

• Added new argument newoutcome = NULL (defult). This parameter is used in predict (.SEM, .DNN, .ML) functions to predict a sink categorical node (as a factor) for classification purposes using all graph nodes as covariates.

• classificationReport() function. A report showing the main classification metrics, like precision, recall, F1-score, accuracy, Matthew’s correlation coefficient (mcc) for all classes of the node = as.factor(outcome).

• crossValidation() function. A R-repeated K-fold cross-validation with a list of M models from SEMrun(), SEMml() and SEMdnn(). The winning model is selected by reporting the mean predicted performances across all RxKxM runs.

• getVariableImportance() function. Extraction of common Machine Learning (ML) variable (predictor) importance measures after fitting SEMrun(), SEMml() or SEMdnn() models.

• Added new argument nboot = 0 (default). This parameter implements cheap bootstrapping in SEMdnn() and SEMml() functions to generate uncertainties, i.e. CIs, for DNN/ML parameters. Bootstrapping can be enabled by setting a small number (from 1 to 10) of bootstrap samples.

• Change argument thr = 0.5 * max(abs(parameters)) (default). Now the DAG can be colored using a numeric [0-1] threshold. For example, 1/0.5 = 2, can be interpreted as the number of times a node/edge parameter is less than the maximum parameter value.

• Various fixed bugs discovered after the release 0.1.0.

Version 0.1.0 Release Notes

• First stable version on CRAN