# Prediction Explanation with Dependence-Aware Shapley Values

## Documentation for package ‘shapr’ version 1.0.7

- 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 | 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
print.summary.shapr | Print Method for summary.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