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ramps (version 0.6-2)

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

0.6-2

License

GPL 2

Maintainer

Brian Smith

Last Published

March 13th, 2023

Functions in ramps (0.6-2)

Spatial Correlation Structure Classes

Dataset of USGS NURE Uranium Measurements

Expand MCMC Samples for georamps Model Fits

Non-Separable Exponential Spatio-Temporal Correlation Structure

Initialization of georamps Model Parameters

genUSStateSites

Generating Random Sites in a US State

Gneiting Spatial Correlation Structure

Powered Exponential Spatial Correlation Structure

Dataset of Simulated Measurements from JSS Publication

Exponential Spatial Correlation Structure

Gaussian Spatial Correlation Structure

Posterior Summaries of georamps Model Fits

Spherical Spatial Correlation Structure

Bayesian Geostatistical Model Fitting with RAMPS

Auxiliary for Controlling georamps Model Fitting

Non-Separable Powered Exponential Spatio-Temporal Correlation Structure

Rational Quadratic Spatial Correlation Structure

Deviance Information Criterion

Non-Separable Temporally Integrated Powered Exponential Spatial Correlation Structure

Matern Spatial Correlation Structure

Linear Spatial Correlation Structure

Generating a Grid over a US State

Prediction Method for georamps Model Fits

Subsetting of MCMC Sampler Results

Posterior Spatial Distribution Plots

Sine Wave Spatial Correlation Structure