Man pages for NorskRegnesentral/shapr
Prediction Explanation with Dependence-Aware Shapley Values
| aicc_full_cpp | AICc formula for several sets, alternative definition |
| aicc_full_single_cpp | Temp-function for computing the full AICc with several X's... |
| categorical_to_one_hot_layer | A 'torch::nn_module()' Representing a... |
| check_groups | Check that the group parameter has the right form and content |
| compute_MSEv_eval_crit | Mean Squared Error of the Contribution Function 'v(S)' |
| compute_shapley_new | Compute shapley values |
| compute_vS | Computes 'v(S)' for all features subsets 'S'. |
| correction_matrix_cpp | correction term with trace_input in AICc formula |
| create_ctree | Make all conditional inference trees |
| default_doc | Unexported documentation helper function. |
| default_doc_explain | Exported documentation helper function. |
| explain | Explain the output of machine learning models with more... |
| explain_forecast | Explain a forecast from a time series model using Shapley... |
| feature_combinations | Define feature combinations, and fetch additional information... |
| feature_group | Analogue to feature_exact, but for groups instead. |
| feature_group_not_exact | Analogue to feature_not_exact, but for groups instead. |
| feature_matrix_cpp | Get feature matrix |
| finalize_explanation | Computes the Shapley values given 'v(S)' |
| gauss_cat_loss | A 'torch::nn_module()' Representing a 'gauss_cat_loss' |
| gauss_cat_parameters | A 'torch::nn_module()' Representing a 'gauss_cat_parameters' |
| gauss_cat_sampler_most_likely | A 'torch::nn_module()' Representing a... |
| gauss_cat_sampler_random | A 'torch::nn_module()' Representing a... |
| gaussian_transform | Transforms a sample to standardized normal distribution |
| gaussian_transform_separate | Transforms new data to standardized normal (dimension 1)... |
| get_cov_mat | get_cov_mat |
| get_data_forecast | Set up data for explain_forecast |
| get_data_specs | Fetches feature information from a given data set |
| get_extra_parameters | This includes both extra parameters and other objects |
| get_model_specs | Fetches feature information from natively supported models |
| get_mu_vec | get_mu_vec |
| get_predict_model | Get predict_model function |
| get_supported_approaches | Gets the implemented approaches |
| get_supported_models | Provides a data.table with the supported models |
| hat_matrix_cpp | Computing single H matrix in AICc-function using the... |
| inv_gaussian_transform_cpp | Transforms new data to a standardized normal distribution |
| lag_data | Lag a matrix of variables a specific number of lags for each... |
| mahalanobis_distance_cpp | (Generalized) Mahalanobis distance |
| mcar_mask_generator | Missing Completely at Random (MCAR) Mask Generator |
| memory_layer | A 'torch::nn_module()' Representing a Memory Layer |
| model_checker | Check that the type of model is supported by the native... |
| observation_impute | Generate permutations of training data using test... |
| observation_impute_cpp | Get imputed data |
| paired_sampler | Sampling Paired Observations |
| plot_MSEv_eval_crit | Plots of the MSEv Evaluation Criterion |
| plot.shapr | Plot of the Shapley value explanations |
| plot_SV_several_approaches | Shapley value bar plots for several explanation objects |
| predict_model | Generate predictions for input data with specified model |
| prepare_data | Generate data used for predictions and Monte Carlo... |
| prepare_data_copula_cpp | Generate (Gaussian) Copula MC samples |
| prepare_data_gaussian_cpp | Generate Gaussian MC samples |
| process_factor_data | Treat factors as numeric values |
| quantile_type7_cpp | Compute the quantiles using quantile type seven |
| reg_forecast_setup | Set up exogenous regressors for explanation in a forecast... |
| release_questions | Auxiliary function for the vaeac vignette |
| rss_cpp | sigma_hat_sq-function |
| sample_combinations | Helper function to sample a combination of training and... |
| sample_ctree | Sample ctree variables from a given conditional inference... |
| setup | check_setup |
| setup_approach | Set up the framework chosen approach |
| setup_computation | Sets up everything for the Shapley values computation in... |
| shapley_weights | Calculate Shapley weight |
| shapr-package | shapr: Prediction Explanation with Dependence-Aware Shapley... |
| skip_connection | A 'torch::nn_module()' Representing a skip connection |
| specified_masks_mask_generator | A 'torch::nn_module()' Representing a... |
| specified_prob_mask_generator | A 'torch::nn_module()' Representing a... |
| test_predict_model | Model testing function |
| vaeac | Initializing a vaeac model |
| vaeac_categorical_parse_params | Creates Categorical Distributions |
| vaeac_check_activation_func | Function that checks the provided activation function |
| vaeac_check_cuda | Function that checks for access to CUDA |
| vaeac_check_epoch_values | Function that checks provided epoch arguments |
| vaeac_check_extra_named_list | Check vaeac.extra_parameters list |
| vaeac_check_logicals | Function that checks logicals |
| vaeac_check_mask_gen | Function that checks the specified masking scheme |
| vaeac_check_masking_ratio | Function that checks that the masking ratio argument is valid |
| vaeac_check_parameters | Function that calls all vaeac parameters check functions |
| vaeac_check_positive_integers | Function that checks positive integers |
| vaeac_check_positive_numerics | Function that checks positive numerics |
| vaeac_check_probabilities | Function that checks probabilities |
| vaeac_check_save_names | Function that checks that the save folder exists and for a... |
| vaeac_check_save_parameters | Function that gives a warning about disk usage |
| vaeac_check_verbose | Function the checks the verbose parameter |
| vaeac_check_which_vaeac_model | Function that checks for valid 'vaeac' model name |
| vaeac_check_x_colnames | Function that checks the feature names of data and 'vaeac'... |
| vaeac_compute_normalization | Compute Featurewise Means and Standard Deviations |
| vaeac_dataset | Dataset used by the 'vaeac' model |
| vaeac_extend_batch | Extends Incomplete Batches by Sampling Extra Data from... |
| vaeac_get_current_save_state | Function that extracts additional objects from the... |
| 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... |
| vaeac_get_extra_para_default | Function to specify the extra parameters in the 'vaeac' model |
| vaeac_get_full_state_list | Function that extracts the state list objects from the... |
| vaeac_get_mask_generator_name | Function that determines which mask generator to use |
| vaeac_get_model_from_checkp | Function to load a 'vaeac' model and set it in the right... |
| vaeac_get_n_decimals | Function to get string of values with specific number of... |
| vaeac_get_optimizer | Function to create the optimizer used to train 'vaeac' |
| vaeac_get_save_file_names | Function that creates the save file names for the 'vaeac'... |
| vaeac_get_val_iwae | Compute the Importance Sampling Estimator (Validation Error) |
| vaeac_get_x_explain_extended | Function to extend the explicands and apply all relevant... |
| vaeac_impute_missing_entries | Impute Missing Values Using Vaeac |
| vaeac_kl_normal_normal | Compute the KL Divergence Between Two Gaussian Distributions. |
| vaeac_normalize_data | Normalize mixed data for 'vaeac' |
| vaeac_normal_parse_params | Creates Normal Distributions |
| 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_postprocess_data | Postprocess Data Generated by a vaeac Model |
| vaeac_prep_message_batch | Produce message about which batch prepare_data is working on |
| vaeac_preprocess_data | Preprocess Data for the vaeac approach |
| vaeac_print_train_summary | Function to printout a training summary for the 'vaeac' model |
| vaeac_save_state | Function that saves the state list and the current save state... |
| vaeac_train_model | Train the Vaeac Model |
| vaeac_train_model_auxiliary | Function used to train a 'vaeac' model |
| vaeac_train_model_continue | Continue to Train the vaeac Model |
| vaeac_update_para_locations | Move 'vaeac' parameters to correct location |
| vaeac_update_pretrained_model | Function that checks and adds a pre-trained 'vaeac' model |
| weight_matrix | Calculate weighted matrix |
| weight_matrix_cpp | Calculate weight matrix |
