explain | Explain the output of machine learning models with dependence-aware (conditional/observational) Shapley values |
explain_forecast | Explain a forecast from time series models with dependence-aware (conditional/observational) Shapley values |
get_extra_comp_args_default | Gets the default values for the extra computation arguments |
get_iterative_args_default | Function to specify arguments of the iterative estimation procedure |
get_output_args_default | Gets the default values for the output arguments |
get_supported_approaches | Gets the implemented approaches |
get_supported_models | Provides a data.table with the supported models |
plot.shapr | Plot of the Shapley value explanations |
plot_MSEv_eval_crit | Plots of the MSEv Evaluation Criterion |
plot_SV_several_approaches | Shapley value bar plots for several explanation objects |
plot_vaeac_eval_crit | Plot the training VLB and validation IWAE for 'vaeac' models |
plot_vaeac_imputed_ggpairs | Plot Pairwise Plots for Imputed and True Data |
print.shapr | Print method for shapr objects |
vaeac_get_extra_para_default | Function to specify the extra parameters in the 'vaeac' model |
vaeac_train_model_continue | Continue to Train the vaeac Model |