All functions

compute_vS()

Computes v(S) for all features subsets S.

explain()

Explain the output of machine learning models with more accurately estimated Shapley values

explain_forecast()

Explain a forecast from a time series model using Shapley values.

feature_combinations()

Define feature combinations, and fetch additional information about each unique combination

finalize_explanation()

Computes the Shapley values given v(S)

get_cov_mat()

get_cov_mat

get_data_forecast()

Set up data for explain_forecast

get_mu_vec()

get_mu_vec

get_supported_approaches()

Gets the implemented approaches

lag_data()

Lag a matrix of variables a specific number of lags for each variables.

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

process_factor_data()

Treat factors as numeric values

reg_forecast_setup()

Set up exogenous regressors for explanation in a forecast model.

release_questions()

Auxiliary function for the vaeac vignette

setup()

check_setup

setup_approach()

Set up the framework chosen approach

setup_computation()

Sets up everything for the Shapley values computation in explain()

vaeac_get_data_objects()

Function to set up data loaders and save file names

vaeac_get_evaluation_criteria()

Extract the Training VLB and Validation IWAE from a list of explanations objects using the vaeac approach

vaeac_get_extra_para_default()

Function to specify the extra parameters in the vaeac model

vaeac_plot_eval_crit()

Plot the training VLB and validation IWAE for vaeac models

vaeac_plot_imputed_ggpairs()

Plot Pairwise Plots for Imputed and True Data

vaeac_train_model()

Train the Vaeac Model

vaeac_train_model_continue()

Continue to Train the vaeac Model