Geostatistical models for the GeoStats.jl framework
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Updated
Dec 15, 2025 - Julia
Geostatistical models for the GeoStats.jl framework
Geostatistical functions for the GeoStats.jl framework
Transparent Python workflow for experimental variograms, simple kriging, and block bootstrap uncertainty in resource-style grids.
A Rust implementation of the core algorithms of GSTools.
Gaussian process regression
spatial dependence tutorial 2; load point data from CSVs, measure potential spatial dependence
Repository for Variogram, Correlation and Kriging Estimation (VarioCorreKrigE)
GSTools - A geostatistical toolbox: random fields, variogram estimation, covariance models, kriging and much more
User-friendly python app to create radioactivity maps using Kriging
"Space-Time Interpolation and Forecasting" - Predicting spatio-temporally distributed variables via space-time regression kriging using numpy and numba.
GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in May, 6th: Drift analysis.
Variography for the GeoStats.jl framework
Built-in solvers for the GeoStats.jl framework
Kriging estimators for the GeoStats.jl framework
(Geo)spatial Statistics with R (Meuse)
Trend Surface Analysis with R (Cape Flats Aquifer)
Nonparametric functional data analysis
A tool to quantify the spatio-temporal continuity of sparse data
Applied Spatial Data Analysis in geoscience
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