Prediction Explanation with Dependence-Aware Shapley Values

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Documentation for package ‘shapr’ version 1.0.6

• 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
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