All the courses presented here are systematically available under Python (jupyter-notebook) or R (Rmarkdown) platforms.
Use the adequate link in the header of each figure.

Getting Started (Python / R)

This script is not actually a course. It simply serves to check if gstlearn library has been installed correctly.

Data Base (Python / R)

Description of the Data Base (numerical). How to create a Db (from different sources), to use shortcuts (assessors), to assign roles to variables (locators).

Graphics (Python / R)

Use the library gtlearn.plot (based on matplotlib) to visualize all the objects of gstlearn library

Variography (Python / R)

Calculate variograms on one or several variables of a Db, for any space dimension. Fit a Model (automatic procedure)

Kriging (Python / R)

Perform estimation of a target variable using Kriging: various options (simple and ordinary). Cross-validation.

Multivariate (Python / R)

Estimation using several variables simultaneously: Cokriging, Kriging with trend, Kriging with External Drift

Factorial Kriging Analysis (Python / R)

Filtering images using Factorial Kriging Analysis. Example on a multivariate acquisition

Simulations (Python / R)

Simulations using Turning Bands method: derive the probability of exceeding a threshold. Extension when using an External Drift

Conditional Expectation (Python / R)

Normal transform (Gaussian anamorphosis). Deriving conditional expectation of exceeding a threshold

PluriGaussian simulations (Python / R)

Categorical Simulations using PluriGaussian model. Automatic model fitting. Connectivity test (using acceptation-rejection technique)

SPDE (Python / R)

Using Stochastic Partial Derivative Equation framework to perform Estimation (extension to the use of External Drift)