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geostatsp (version 2.0.10)

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

2.0.10

License

GPL

Maintainer

Patrick Brown

Last Published

February 26th, 2026

Functions in geostatsp (2.0.10)

krigeLgm

Spatial prediction, or Kriging
likfitLgm

Likelihood Based Parameter Estimation for Gaussian Random Fields
inla.models

Valid models in INLA
conditionalGmrf

Conditional distribution of GMRF
gambiaUTM

Gambia data
spatialRoc

Sensitivity and specificity
matern

Evaluate the Matern correlation function
squareRaster-methods

Create a raster with square cells
postExp

Exponentiate posterior quantiles
profLlgm

Joint confidence regions
simLgcp

Simulate a log-Gaussian Cox process
pcPriorRange

PC prior for range parameter
maternGmrfPrec

Precision matrix for a Matern spatial correlation
rongelapUTM

Rongelap data
murder

Murder locations
variog

Compute Empirical Variograms and Permutation Envelopes
swissRainR

Raster of Swiss rain data
stackRasterList

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

Mercer and Hall wheat yield data
swissRain

Swiss rainfall data
loaloa

Loaloa prevalence data from 197 village surveys
excProb

Exceedance probabilities
glgm-methods

Generalized Linear Geostatistical Models
lgm-methods

Linear Geostatistical Models
RFsimulate

Simulation of Random Fields