Learn R Programming

spatial.gev.bma (version 1.0)

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

Description

This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.

Copy Link

Version

Install

install.packages('spatial.gev.bma')

Monthly Downloads

2

Version

1.0

License

GPL

Maintainer

Alex Lenkoski

Last Published

May 21st, 2014

Functions in spatial.gev.bma (1.0)

gev.like

The log likelihood of a GEV distribution
gev.results.init

Initialize a results object for spatial.bma.gev
gev.init

Initilizes a state object for a Spatial GEV distribution
gev.logscore

Compute the Log Score
dmvnorm

Log density of a multivariate normal distribution
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
gev.update.tau.xi

Update the random effects for the shape parameter in a spatial GEV model
f.prime

First derivative of the posterior of a spatial GEV model with respect to a random effect in the location parameter.
gev.update.tau.kappa

Update the random effects of the precision parameter in a spatial GEV model
l.double.prime

The second derivative of a Gaussian process with respect to the parameter lambda.
norway

Extreme Precipitation Data at 69 Sites in Norway
make.D

Form the distance matrix for use in a Gaussian Process
f.double.prime

Second derivative of the posterior distribution of a spatial GEV with respect a location random effect
gev.update.hyper

Updates the Gaussian Process hyperparameters in the Spatial GEV model
gev.impute

Given the output of the MCMC, return a number of samples for a new site.
gev.update.lambda

Update the lambda parameter in a Gaussian Process
g.double.prime

The second derivative of a GEV distribution with respect to a random effect parameter on the precision kappa
l.prime

First derivative of a GP with respect to lambda
spatial.gev.bma

Run an MCMC to fit a hierarchical spatial generalized extreme value (GEV) model with the option for Bayesian model averaging (BMA)
gev.update.M

Sample a new model from the current model for any linear regression system
gev.update.tau.mu

Internal function to update the random effects of the location parameter in a Spatial GEV model.
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.
spatial.gev.bma-package

Fit a Hierarchical Spatial Generalized Extreme Value model that allows for Bayesian Model Averaging
j.prime

The first derivative of the posterior density of a spatial GEV model with respect to a random effect parameter on the shape.
gev.crps

Compute the Continuous Rank Probability Score (CRPS)
gev.update.theta

Update the linear parameters in a spatial GEV model
gev.update

Updates all the parameters in a spatial GEV model
gev.process.results

Outputs some tables from the results of Spatial GEV MCMC run
j.double.prime

The second derivative of a spatial GEV with respect to a random effect in the shape parameter
logdet

Returns the log determinant for a symmetric positive definite matrix.