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ramps (version 0.6.18)

Bayesian Geostatistical Modeling with RAMPS

Description

Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples. Package performance is tuned for large spatial datasets.

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Version

Install

install.packages('ramps')

Monthly Downloads

380

Version

0.6.18

License

GPL-2

Maintainer

Brian Smith

Last Published

March 13th, 2023

Functions in ramps (0.6.18)

corRExpwr

Powered Exponential Spatial Correlation Structure
DIC

Deviance Information Criterion
corRClasses

Spatial Correlation Structure Classes
corRExp

Exponential Spatial Correlation Structure
corRExpwr2

Non-Separable Powered Exponential Spatio-Temporal Correlation Structure
corRCauchy

Cauchy Spatial Correlation Structure
corRExp2

Non-Separable Exponential Spatio-Temporal Correlation Structure
NURE

Dataset of USGS NURE Uranium Measurements
corRExpwr2Dt

Non-Separable Temporally Integrated Powered Exponential Spatial Correlation Structure
corRGaus

Gaussian Spatial Correlation Structure
corRSpher

Spherical Spatial Correlation Structure
georamps

Bayesian Geostatistical Model Fitting with RAMPS
param

Initialization of georamps Model Parameters
corRWave

Sine Wave Spatial Correlation Structure
corRGneit

Gneiting Spatial Correlation Structure
expand.chain

Expand MCMC Samples for georamps Model Fits
corRMatern

Matern Spatial Correlation Structure
genUSStateSites

Generating Random Sites in a US State
corRLin

Linear Spatial Correlation Structure
genUSStateGrid

Generating a Grid over a US State
summary.ramps

Posterior Summaries of georamps Model Fits
plot

Posterior Spatial Distribution Plots
simJSS

Dataset of Simulated Measurements from JSS Publication
ramps.control

Auxiliary for Controlling georamps Model Fitting
predict.ramps

Prediction Method for georamps Model Fits
window

Subsetting of MCMC Sampler Results