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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

283

Version

0.6.18

License

GPL-2

Maintainer

Brian Smith

Last Published

March 13th, 2023

Functions in ramps (0.6.18)

Powered Exponential Spatial Correlation Structure

Deviance Information Criterion

Spatial Correlation Structure Classes

Exponential Spatial Correlation Structure

Non-Separable Powered Exponential Spatio-Temporal Correlation Structure

Cauchy Spatial Correlation Structure

Non-Separable Exponential Spatio-Temporal Correlation Structure

Dataset of USGS NURE Uranium Measurements

Non-Separable Temporally Integrated Powered Exponential Spatial Correlation Structure

Gaussian Spatial Correlation Structure

Spherical Spatial Correlation Structure

Bayesian Geostatistical Model Fitting with RAMPS

Initialization of georamps Model Parameters

Sine Wave Spatial Correlation Structure

Gneiting Spatial Correlation Structure

Expand MCMC Samples for georamps Model Fits

Matern Spatial Correlation Structure

genUSStateSites

Generating Random Sites in a US State

Linear Spatial Correlation Structure

Generating a Grid over a US State

Posterior Summaries of georamps Model Fits

Posterior Spatial Distribution Plots

Dataset of Simulated Measurements from JSS Publication

Auxiliary for Controlling georamps Model Fitting

Prediction Method for georamps Model Fits

Subsetting of MCMC Sampler Results