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_results             Extract components from a shapr object
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
summary.shapr           Summary 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
