Package index • shapr

All functions
`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 estimation 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

Reference