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geostatsp (version 2.0.11)

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

367

Version

2.0.11

License

GPL

Maintainer

Patrick Brown

Last Published

February 26th, 2026

Functions in geostatsp (2.0.11)

murder

Murder locations
simLgcp

Simulate a log-Gaussian Cox process
pcPriorRange

PC prior for range parameter
maternGmrfPrec

Precision matrix for a Matern spatial correlation
wheat

Mercer and Hall wheat yield data
postExp

Exponentiate posterior quantiles
swissRainR

Raster of Swiss rain data
variog

Compute Empirical Variograms and Permutation Envelopes
squareRaster-methods

Create a raster with square cells
swissRain

Swiss rainfall data
stackRasterList

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

Joint confidence regions
conditionalGmrf

Conditional distribution of GMRF
lgm-methods

Linear Geostatistical Models
krigeLgm

Spatial prediction, or Kriging
inla.models

Valid models in INLA
likfitLgm

Likelihood Based Parameter Estimation for Gaussian Random Fields
RFsimulate

Simulation of Random Fields
excProb

Exceedance probabilities
glgm-methods

Generalized Linear Geostatistical Models
gambiaUTM

Gambia data
loaloa

Loaloa prevalence data from 197 village surveys
matern

Evaluate the Matern correlation function
rongelapUTM

Rongelap data
spatialRoc

Sensitivity and specificity