Regularized Point Processes and Stochastic Marginalization for Extremes

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Documentation for package ‘margEVT’ version 0.6.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

active_covariates	Extract names of non-seasonal covariates from a fitted model
annual_prob	Annual non-exceedance probability under a covariate law
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_nhpp	Parametric bootstrap for return levels and coefficients in one pass
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
coef.nhpp_fit	Extract coefficients of a fitted nhpp_fit model
confint.nhpp_fit	Wald covariance matrix and confidence intervals for nhpp_fit
decluster	Decluster a series of threshold exceedances
design_life_level	Design life level over a planning horizon
extremal_index	Extremal index of a series of threshold exceedances
fitted.nhpp_fit	Log-likelihood, number of observations, and prediction for nhpp_fit
fit_nhpp	Fit a non-homogeneous point process model for extremes
fit_var_generator	Fit a stationary VAR generator of covariate trajectories
horizon_risk	Exceedance risk over a planning horizon
is_nhpp_fit	Check if an object is an nhpp_fit
logLik.nhpp_fit	Log-likelihood, number of observations, and prediction for nhpp_fit
marginalize	Compute unconditional return levels by marginalizing over covariates
nhpp_fit-methods	Log-likelihood, number of observations, and prediction for nhpp_fit
nhpp_fit-wald	Wald covariance matrix and confidence intervals for nhpp_fit
nhpp_horizon-methods	Print and plot results of horizon_risk()
nobs.nhpp_fit	Log-likelihood, number of observations, and prediction for nhpp_fit
n_exceedances	Extract the number of exceedances from a fitted model
plot.nhpp_fit	Plot diagnostics for a fitted nhpp_fit object
plot.nhpp_horizon	Print and plot results of horizon_risk()
plot.nhpp_rl	Plot return-level curves from marginalize()
pp_grad	Analytical gradient of the penalized negative log-likelihood
pp_nllh	Penalized negative log-likelihood for the NHPP model
predict.nhpp_fit	Log-likelihood, number of observations, and prediction for nhpp_fit
predict_params	Compute time-varying GEV parameters from a fitted model
print.nhpp_backtest	Print results of backtest()
print.nhpp_boot	Print results of bootstrap_nhpp()
print.nhpp_fit	Print a fitted nhpp_fit model
print.nhpp_horizon	Print and plot results of horizon_risk()
print.nhpp_var_generator	Print a fitted VAR covariate generator
print.summary.nhpp_fit	Print a summary of a fitted nhpp_fit model
refit_nhpp	Refit an NHPP model on new data with the same specification
residuals.nhpp_fit	Transformed residuals of a fitted nhpp_fit model
rl_table	Wide-format return level table from marginalize() output
seasonal_harmonics	Seasonal Fourier harmonics
simulate.nhpp_fit	Simulate series from a fitted nhpp_fit model
simulate_covariates	Simulate stationary covariate trajectories from a VAR generator
summary.nhpp_fit	Summarise a fitted nhpp_fit model
vcov.nhpp_fit	Wald covariance matrix and confidence intervals for nhpp_fit