Prediction Explanation with Dependence-Aware Shapley Values
![[R logo]](../../../doc/html/Rlogo.svg)
![[Up]](../../../doc/html/left.jpg)
![[Top]](../../../doc/html/up.jpg)
Documentation for package ‘shapr’ version 1.0.4
Help Pages
| 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 |