minigst (R)

The companion R package for gstlearn.

This R package wraps gstlearn functions to offer access to some basic geostatistical methods (mainly variography and kriging).

You need R 4.0 or higher. You will also need to install the gstlearn R package. Please refer to the gstlearn documentation for installation instructions.

You can install the official release of minigst package using the following R command:

install.packages("minigst", repos="https://soft.mines-paristech.fr/cran")

After cloning the github repo, you can install the minigst package from source using the following shell command:

git clone https://github.com/gstlearn/minigst
cd minigst
cd R
R CMD INSTALL .

library(minigst)

# Load data from a pandas DataFrame
df = read.csv("data.csv")
db = dfToDb(df, coord_names=["x", "y"])

# Compute experimental variogram
vario_exp = vario_exp(db, vname="variable", nlag=20, dlag=10.0)

# Fit a model
model = model_fit(vario_exp, struct=["NUGGET", "SPHERICAL"])

# Perform kriging
target_db = createDbGrid(nx=[100, 100], dx=[1.0, 1.0])
minikriging(db, target_db, vname="variable", model=model)

# Plot results
dbplot_grid(target_db, color="K.variable.estim")

The minigst R package provides wrapper functions for:

• Database operations: Convert pandas DataFrames to gstlearn Db objects, create grids, manipulate variables
• Plotting: Visualize spatial data and grids using matplotlib
• Variography: Compute experimental variograms and fit models
• Kriging: Perform simple, ordinary, and universal kriging
• Simulation: Generate Gaussian random fields

For more information about the underlying gstlearn library, please visit gstlearn.org.

This package is distributed under the GPL license.