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geostatsp (version 2.0.8)

Geostatistical Modelling with Likelihood and Bayes

Description

Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector' objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) . The 'RandomFields' package is available at .

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Version

Install

install.packages('geostatsp')

Monthly Downloads

701

Version

2.0.8

License

GPL

Maintainer

Patrick Brown

Last Published

February 7th, 2025

Functions in geostatsp (2.0.8)

Joint confidence regions

Exponentiate posterior quantiles

Simulate a log-Gaussian Cox process

squareRaster-methods

Create a raster with square cells

PC prior for range parameter

Evaluate the Matern correlation function

Murder locations

Sensitivity and specificity

stackRasterList

Converts a list of rasters, possibly with different projections and resolutions, to a single raster stack.

Compute Empirical Variograms and Permutation Envelopes

Precision matrix for a Matern spatial correlation

Raster of Swiss rain data

Swiss rainfall data

Mercer and Hall wheat yield data

Likelihood Based Parameter Estimation for Gaussian Random Fields

Spatial prediction, or Kriging

Valid models in INLA

Linear Geostatistical Models

Loaloa prevalence data from 197 village surveys

Simulation of Random Fields

Generalized Linear Geostatistical Models

Exceedance probabilities

conditionalGmrf

Conditional distribution of GMRF