Regularized Point Processes and Stochastic Marginalization for Extremes

Documentation for package ‘margEVT’ version 0.1.0

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
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