| batch_size_default | Default batch size (internal) |
| check_directory | Check directory existence |
| compute_EQRN_GPDLoss | Generalized Pareto likelihood loss of a EQRN_iid predictor |
| compute_EQRN_seq_GPDLoss | Generalized Pareto likelihood loss of a EQRN_seq predictor |
| decay_learning_rate | Performs a learning rate decay step on an optimizer |
| default_device | Default torch device |
| end_doFuture_strategy | End the currently set doFuture strategy |
| EQRN_excess_probability | Tail excess probability prediction using an EQRN_iid object |
| EQRN_excess_probability_seq | Tail excess probability prediction using an EQRN_seq object |
| EQRN_fit | EQRN fit function for independent data |
| EQRN_fit_restart | Wrapper for fitting EQRN with restart for stability |
| EQRN_fit_seq | EQRN fit function for sequential and time series data |
| EQRN_load | Load an EQRN object from disc |
| EQRN-package | EQRN: Extreme Quantile Regression Neural Networks for Risk... |
| EQRN_predict | Predict function for an EQRN_iid fitted object |
| EQRN_predict_internal | Internal predict function for an EQRN_iid |
| EQRN_predict_internal_seq | Internal predict function for an EQRN_seq fitted object |
| EQRN_predict_params | GPD parameters prediction function for an EQRN_iid fitted... |
| EQRN_predict_params_seq | GPD parameters prediction function for an EQRN_seq fitted... |
| EQRN_predict_seq | Predict function for an EQRN_seq fitted object |
| EQRN_save | Save an EQRN object on disc |
| excess_probability | Excess Probability Predictions |
| excess_probability.EQRN_iid | Tail excess probability prediction method using an EQRN_iid... |
| excess_probability.EQRN_seq | Tail excess probability prediction method using an EQRN_iid... |
| FC_GPD_net | MLP module for GPD parameter prediction |
| FC_GPD_SNN | Self-normalized fully-connected network module for GPD... |
| fit_GPD_unconditional | Maximum likelihood estimates for the GPD distribution using... |
| fix_dimsimplif | (INTERNAL) Corrects a dimension simplification bug from the... |
| get_doFuture_operator | Get doFuture operator |
| get_excesses | Computes rescaled excesses over the conditional quantiles |
| GPD_excess_probability | Tail excess probability prediction based on conditional GPD... |
| GPD_quantiles | Compute extreme quantile from GPD parameters |
| install_backend | Install Torch Backend |
| instantiate_EQRN_network | Instantiates the default networks for training a EQRN_iid... |
| lagged_features | Covariate lagged replication for temporal dependence |
| last_elem | Last element of a vector |
| legacy_names | Internal renaming function for back-compatibility |
| list2matrix | Convert a list to a matrix |
| loss_GPD | Generalized Pareto likelihood loss |
| loss_GPD_tensor | GPD tensor loss function for training a EQRN network |
| make_folds | Create cross-validation folds |
| mean_absolute_error | Mean absolute error |
| mean_squared_error | Mean squared error |
| mts_dataset | Dataset creator for sequential data |
| multilevel_exceedance_proba_error | Multilevel 'quantile_exceedance_proba_error' |
| multilevel_MAE | Multilevel quantile MAEs |
| multilevel_MSE | Multilevel quantile MSEs |
| multilevel_pred_bias | Multilevel prediction bias |
| multilevel_prop_below | Multilevel 'proportion_below' |
| multilevel_q_loss | Multilevel quantile losses |
| multilevel_q_pred_error | Multilevel 'quantile_prediction_error' |
| multilevel_resid_var | Multilevel residual variance |
| multilevel_R_squared | Multilevel R squared |
| nn_alpha_dropout | Alpha-dropout module |
| nn_dropout_nd | Dropout module |
| onload_backend_installer | On-Load Torch Backend Internal Install helper |
| perform_scaling | Performs feature scaling without overfitting |
| predict.EQRN_iid | Predict method for an EQRN_iid fitted object |
| predict.EQRN_seq | Predict method for an EQRN_seq fitted object |
| predict_GPD_semiconditional | Predict semi-conditional extreme quantiles using peaks over... |
| prediction_bias | Prediction bias |
| prediction_residual_variance | Prediction residual variance |
| predict.QRN_seq | Predict method for a QRN_seq fitted object |
| predict_unconditional_quantiles | Predict unconditional extreme quantiles using peaks over... |
| process_features | Feature processor for EQRN |
| proportion_below | Proportion of observations below conditional quantile vector |
| QRN_fit_multiple | Wrapper for fitting a recurrent QRN with restart for... |
| QRNN_RNN_net | Recurrent quantile regression neural network module |
| QRN_seq_fit | Recurrent QRN fitting function |
| QRN_seq_predict | Predict function for a QRN_seq fitted object |
| QRN_seq_predict_foldwise | Foldwise fit-predict function using a recurrent QRN |
| QRN_seq_predict_foldwise_sep | Sigle-fold foldwise fit-predict function using a recurrent... |
| quantile_exceedance_proba_error | Quantile exceedance probability prediction calibration error |
| quantile_loss | Quantile loss |
| quantile_loss_tensor | Tensor quantile loss function for training a QRN network |
| quantile_prediction_error | Quantile prediction calibration error |
| Recurrent_GPD_net | Recurrent network module for GPD parameter prediction |
| roundm | Mathematical number rounding |
| R_squared | R squared |
| safe_save_rds | Safe RDS save |
| semiconditional_train_valid_GPD_loss | Semi-conditional GPD MLEs and their train-validation... |
| Separated_GPD_SNN | Self-normalized separated network module for GPD parameter... |
| set_doFuture_strategy | Set a doFuture execution strategy |
| setup_optimizer | Instantiate an optimizer for training an EQRN_iid network |
| setup_optimizer_seq | Instantiate an optimizer for training an EQRN_seq network |
| square_loss | Square loss |
| unconditional_train_valid_GPD_loss | Unconditional GPD MLEs and their train-validation likelihoods |
| vec2mat | Convert a vector to a matrix |
| vector_insert | Insert value in vector |
