R: Regularized Point Processes and Stochastic Marginalization for Extremes

# Regularized Point Processes and Stochastic Marginalization for Extremes

## Documentation for package ‘margEVT’ version 0.1.1

- DESCRIPTION file
- Package README

## Help Pages

active_covariates | Extract names of non-seasonal covariates from a fitted model
backtest | Walk-forward backtesting for NHPP extreme value models
bic_nhpp | Compute BIC for a fitted nhpp_fit model
bootstrap_coef | Parametric bootstrap confidence intervals for model coefficients
bootstrap_rl | Parametric bootstrap confidence intervals for return levels
build_cov_annual | Build a covariate data frame for marginalization
build_design_matrices | Build design matrices for the NHPP point process model
fit_nhpp | Fit a non-homogeneous point process model for extremes
fit_var_generator | Fit a generic VAR generator over a model's active covariates
is_nhpp_fit | Check if an object is an nhpp_fit
marginalize | Compute unconditional return levels by marginalizing over covariates
n_exceedances | Extract the number of exceedances from a fitted model
plot.nhpp_fit | Plot diagnostics for a fitted nhpp_fit object
pp_grad | Analytical gradient of the penalized negative log-likelihood
pp_nllh | Penalized negative log-likelihood for the NHPP model
predict_params | Compute time-varying GEV parameters from a fitted model
rl_table | Wide-format return level table from marginalize() output
simulate_covariates | Simulate stationary covariate trajectories from a VAR generator
summary.nhpp_fit | Summarise a fitted nhpp_fit model