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
                        Get 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
                        Get the Default Values for the Output Arguments
get_results             Extract Components from a Shapr Object
get_supported_approaches
                        Get the Implemented Approaches
get_supported_models    Provide 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
print.summary.shapr     Print Method for summary.shapr Objects
summary.shapr           Summary Method for Shapr Objects
vaeac_get_extra_para_default
                        Specify the Extra Parameters in the 'vaeac'
                        Model
vaeac_train_model_continue
                        Continue to Train the 'vaeac' Model
