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_predict            Predict function for an EQRN_iid fitted object
EQRN_predict_params     GPD parameters prediction function for an
                        EQRN_iid fitted object
EQRN_predict_params_seq
                        GPD parameters prediction function for an
                        EQRN_seq fitted object
EQRN_predict_seq        Predict function for an EQRN_seq fitted object
EQRN_save               Save an EQRN object on disc
FC_GPD_SNN              Self-normalized fully-connected network module
                        for GPD parameter prediction
FC_GPD_net              MLP module for GPD parameter prediction
GPD_excess_probability
                        Tail excess probability prediction based on
                        conditional GPD parameters
GPD_quantiles           Compute extreme quantile from GPD parameters
QRNN_RNN_net            Recurrent quantile regression neural network
                        module
QRN_fit_multiple        Wrapper for fitting a recurrent QRN with
                        restart for stability
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 QRN
R_squared               R squared
Recurrent_GPD_net       Recurrent network module for GPD parameter
                        prediction
Separated_GPD_SNN       Self-normalized separated network module for
                        GPD parameter prediction
backend_is_installed    Check if Torch Backend Libraries are Installed
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
default_device          Default torch device
end_doFuture_strategy   End the currently set doFuture strategy
excess_probability      Excess Probability Predictions
excess_probability.EQRN_iid
                        Tail excess probability prediction method using
                        an EQRN_iid object
excess_probability.EQRN_seq
                        Tail excess probability prediction method using
                        an EQRN_iid object
fit_GPD_unconditional   Maximum likelihood estimates for the GPD
                        distribution using peaks over threshold
get_doFuture_operator   Get doFuture operator
get_excesses            Computes rescaled excesses over the conditional
                        quantiles
install_backend         Install Torch Backend Libraries
lagged_features         Covariate lagged replication for temporal
                        dependence
last_elem               Last element of a vector
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_MAE          Multilevel quantile MAEs
multilevel_MSE          Multilevel quantile MSEs
multilevel_R_squared    Multilevel R squared
multilevel_exceedance_proba_error
                        Multilevel 'quantile_exceedance_proba_error'
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
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.QRN_seq         Predict method for a QRN_seq fitted object
predict_GPD_semiconditional
                        Predict semi-conditional extreme quantiles
                        using peaks over threshold
predict_unconditional_quantiles
                        Predict unconditional extreme quantiles using
                        peaks over threshold
prediction_bias         Prediction bias
prediction_residual_variance
                        Prediction residual variance
process_features        Feature processor for EQRN
proportion_below        Proportion of observations below conditional
                        quantile vector
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
roundm                  Mathematical number rounding
safe_save_rds           Safe RDS save
semiconditional_train_valid_GPD_loss
                        Semi-conditional GPD MLEs and their
                        train-validation likelihoods
set_doFuture_strategy   Set a doFuture execution strategy
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
