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ramps (version 0.6-12)

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

301

Version

0.6-12

License

GPL-2

Maintainer

Brian Smith

Last Published

November 12th, 2013

Functions in ramps (0.6-12)

Powered Exponential Spatial Correlation Structure

Non-Separable Powered Exponential Spatio-Temporal Correlation Structure

Sine Wave Spatial Correlation Structure

Prediction Method for georamps Model Fits

Deviance Information Criterion

Non-Separable Exponential Spatio-Temporal Correlation Structure

Spatial Correlation Structure Classes

Dataset of USGS NURE Uranium Measurements

Matern Spatial Correlation Structure

Linear Spatial Correlation Structure

Posterior Spatial Distribution Plots

Cauchy Spatial Correlation Structure

Bayesian Geostatistical Model Fitting with RAMPS

genUSStateSites

Generating Random Sites in a US State

Initialization of georamps Model Parameters

Expand MCMC Samples for georamps Model Fits

Posterior Summaries of georamps Model Fits

Auxiliary for Controlling georamps Model Fitting

Gneiting Spatial Correlation Structure

Exponential Spatial Correlation Structure

Generating a Grid over a US State

Subsetting of MCMC Sampler Results

Dataset of Simulated Measurements from JSS Publication

Spherical Spatial Correlation Structure

Gaussian Spatial Correlation Structure

Non-Separable Temporally Integrated Powered Exponential Spatial Correlation Structure