Man pages for shapr
Prediction Explanation with Dependence-Aware Shapley Values
| additional_regression_setup | Additional setup for regression-based methods |
| 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... |
| append_vS_list | Appends the new vS_list to the prev vS_list |
| categorical_to_one_hot_layer | A 'torch::nn_module()' Representing a... |
| check_categorical_valid_MCsamp | Check that all explicands has at least one valid MC sample in... |
| check_convergence | Checks the convergence according to the convergence threshold |
| check_groups | Check that the group parameter has the right form and content |
| check_verbose | Function that checks the verbose parameter |
| cli_compute_vS | Printing messages in compute_vS with cli |
| cli_iter | Printing messages in iterative procedure with cli |
| cli_startup | Printing startup messages with cli |
| coalition_matrix_cpp | Get coalition matrix |
| compute_estimates | Computes the the Shapley values and their standard deviation... |
| compute_MSEv_eval_crit | Mean Squared Error of the Contribution Function 'v(S)' |
| compute_shapley | Compute shapley values |
| compute_time | Gathers and computes the timing of the different parts of the... |
| compute_vS | Computes 'v(S)' for all features subsets 'S'. |
| convert_feature_name_to_idx | Convert feature names into feature indices |
| correction_matrix_cpp | Correction term with trace_input in AICc formula |
| create_coalition_table | Define coalitions, and fetch additional information about... |
| create_ctree | Build all the conditional inference trees |
| create_marginal_data_cat | Create marginal categorical data for causal Shapley values |
| create_marginal_data_gaussian | Generate marginal Gaussian data using Cholesky decomposition |
| create_marginal_data_training | Function that samples data from the empirical marginal... |
| default_doc_export | Exported documentation helper function. |
| default_doc_internal | Unexported documentation helper function. |
| exact_coalition_table | Get table with all (exact) coalitions |
| explain | Explain the output of machine learning models with... |
| explain_forecast | Explain a forecast from time series models with... |
| finalize_explanation | Gathers the final output to create the explanation object |
| 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_comp_args_default | Gets the default values for the extra computation arguments |
| get_extra_parameters | This includes both extra parameters and other objects |
| get_feature_specs | Gets the feature specifications form the model |
| get_iterative_args_default | Function to specify arguments of the iterative estimation... |
| get_max_n_coalitions_causal | Get the number of coalitions that respects the causal... |
| get_model_specs | Fetches feature information from natively supported models |
| get_mu_vec | get_mu_vec |
| get_output_args_default | Gets the default values for the output arguments |
| get_predict_model | Get predict_model function |
| get_S_causal_steps | Get the steps for generating MC samples for coalitions... |
| get_supported_approaches | Gets the implemented approaches |
| get_supported_models | Provides a data.table with the supported models |
| get_valid_causal_coalitions | Get all coalitions satisfying the causal ordering |
| group_forecast_setup | Set up user provided groups for explanation in a forecast... |
| 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 |
| 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 |
| predict_model | Generate predictions for input data with specified model |
| prepare_data | Generate data used for predictions and Monte Carlo... |
| prepare_data_causal | Generate data used for predictions and Monte Carlo... |
| prepare_data_copula_cpp | Generate (Gaussian) Copula MC samples |
| prepare_data_copula_cpp_caus | Generate (Gaussian) Copula MC samples for the causal setup... |
| prepare_data_gaussian_cpp | Generate Gaussian MC samples |
| prepare_data_gaussian_cpp_caus | Generate Gaussian MC samples for the causal setup with a... |
| prepare_data_single_coalition | Compute the conditional probabilities for a single coalition... |
| prepare_next_iteration | Prepares the next iteration of the iterative sampling... |
| print_iter | Prints iterative information |
| print.shapr | Print method for shapr objects |
| 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... |
| regression.check_namespaces | Check that needed libraries are installed |
| regression.check_parameters | Check regression parameters |
| regression.check_recipe_func | Check 'regression.recipe_func' |
| regression.check_sur_n_comb | Check the 'regression.surrogate_n_comb' parameter |
| regression.check_vfold_cv_para | Check the parameters that are sent to 'rsample::vfold_cv()' |
| regression.cv_message | Produce message about which batch prepare_data is working on |
| regression.get_string_to_R | Convert the string into an R object |
| regression.get_tune | Get if model is to be tuned |
| regression.get_y_hat | Get the predicted responses |
| regression.surrogate_aug_data | Augment the training data and the explicands |
| regression.train_model | Train a tidymodels model via workflows |
| release_questions | Auxiliary function for the vignettes |
| rss_cpp | Function for computing sigma_hat_sq |
| sample_coalitions_cpp_str_paired | We here return a vector of strings/characters, i.e., a... |
| sample_coalition_table | Get table with sampled coalitions |
| sample_combinations | Helper function to sample a combination of training and... |
| sample_ctree | Sample ctree variables from a given conditional inference... |
| save_results | Saves the intermediate results to disk |
| setup | check_setup |
| setup_approach | Set up the framework for the chosen approach |
| shapley_setup | Set up the kernelSHAP framework |
| 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... |
| testing_cleanup | Cleans out certain output arguments to allow perfect... |
| 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_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_postprocess_data | Postprocess Data Generated by a vaeac Model |
| 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 |
