R: Extreme Quantile Regression Neural Networks for Risk Forecasting

# Extreme Quantile Regression Neural Networks for Risk Forecasting

## Documentation for package ‘EQRN’ version 0.1.2

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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
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
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
FC_GPD_net | MLP module for GPD parameter prediction
FC_GPD_SNN | Self-normalized fully-connected network module for GPD parameter prediction
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
GPD_excess_probability | Tail excess probability prediction based on conditional GPD parameters
GPD_quantiles | Compute extreme quantile from GPD parameters
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_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
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
prediction_bias | Prediction bias
prediction_residual_variance | Prediction residual variance
predict_GPD_semiconditional | Predict semi-conditional extreme quantiles using peaks over threshold
predict_unconditional_quantiles | Predict unconditional extreme quantiles using peaks over threshold
process_features | Feature processor for EQRN
proportion_below | Proportion of observations below conditional quantile vector
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
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 likelihoods
Separated_GPD_SNN | Self-normalized separated network module for GPD parameter prediction
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