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geostatsp (version 2.2.0)

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 and .

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Version

Install

install.packages('geostatsp')

Monthly Downloads

272

Version

2.2.0

License

GPL (>= 2)

Homepage

https://diseasemapping.r-forge.r-project.org/

Maintainer

Patrick Brown

Last Published

September 10th, 2026

Functions in geostatsp (2.2.0)

Exceedance probabilities

Prepare observations and covariates for a geostatistical model

Check whether INLA is usable

Generalized Linear Geostatistical Models

Linear Geostatistical Models

Valid models in INLA

Spatial prediction, or Kriging

conditionalGmrf

Conditional distribution of GMRF

Simulation of Random Fields

Evaluate the Matern correlation function

Joint confidence regions

Exponentiate posterior quantiles

Simulate a log-Gaussian Cox process

PC prior for range parameter

Precision matrix for a Matern spatial correlation

Likelihood Based Parameter Estimation for Gaussian Random Fields

Compute Empirical Variograms and Permutation Envelopes

Loaloa prevalence data from 197 village surveys

Murder locations

Raster of Swiss rain data

Swiss rainfall data

stackRasterList

Converts a list of SpatRasters, possibly with different projections and resolutions, to a single multi-layer SpatRaster.

Mercer and Hall wheat yield data

squareRaster-methods

Create a raster with square cells

Sensitivity and specificity