# Prediction Explanation with Dependence-Aware Shapley Values

## Documentation for package ‘shapr’ version 1.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## 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 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