Hierarchical spatial generalized extreme value (GEV) modeling with Bayesian Model Averaging (BMA)

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Documentation for package ‘spatial.gev.bma’ version 1.0

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spatial.gev.bma-package Fit a Hierarchical Spatial Generalized Extreme Value model that allows for Bayesian Model Averaging
dmvnorm Log density of a multivariate normal distribution
f.double.prime Second derivative of the posterior distribution of a spatial GEV with respect a location random effect
f.prime First derivative of the posterior of a spatial GEV model with respect to a random effect in the location parameter.
g.double.prime The second derivative of a GEV distribution with respect to a random effect parameter on the precision kappa
g.prime The first derivative of the posterior density of a spatial GEV model with respect to a given random effect on the precision parameter.
gev.crps Compute the Continuous Rank Probability Score (CRPS)
gev.impute Given the output of the MCMC, return a number of samples for a new site.
gev.init Initilizes a state object for a Spatial GEV distribution
gev.like The log likelihood of a GEV distribution
gev.logscore Compute the Log Score
gev.process.results Outputs some tables from the results of Spatial GEV MCMC run
gev.results.init Initialize a results object for spatial.bma.gev
gev.update Updates all the parameters in a spatial GEV model
gev.update.hyper Updates the Gaussian Process hyperparameters in the Spatial GEV model
gev.update.lambda Update the lambda parameter in a Gaussian Process
gev.update.M Sample a new model from the current model for any linear regression system
gev.update.tau.kappa Update the random effects of the precision parameter in a spatial GEV model
gev.update.tau.mu Internal function to update the random effects of the location parameter in a Spatial GEV model.
gev.update.tau.xi Update the random effects for the shape parameter in a spatial GEV model
gev.update.theta Update the linear parameters in a spatial GEV model
gev.z.p Calculate the 1/p return level for a GEV distribution
gp.like.lambda The likelihood of a Gaussian process used to initialize the lambda parameter
j.double.prime The second derivative of a spatial GEV with respect to a random effect in the shape parameter
j.prime The first derivative of the posterior density of a spatial GEV model with respect to a random effect parameter on the shape.
l.double.prime The second derivative of a Gaussian process with respect to the parameter lambda.
l.prime First derivative of a GP with respect to lambda
logdet Returns the log determinant for a symmetric positive definite matrix.
make.D Form the distance matrix for use in a Gaussian Process
norway Extreme Precipitation Data at 69 Sites in Norway
spatial.gev.bma Run an MCMC to fit a hierarchical spatial generalized extreme value (GEV) model with the option for Bayesian model averaging (BMA)