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(): 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

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