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