Softwares and Tools

GeoKDD members develop software, packages, and extension for popular programming languages such as R and Python. Here we will list some of the most recent contributions by GeoKDD researchers:

_ “FiSh knowledge from your data.” _ (Python/R package)

Authors: Fabian Guignard and Mohamed Laib

Proposes non-parametric estimates of the Fisher Information Measure and the Shannon Entropy Power. The package contains also some bandwidth selectors for kernel density estimate of a function and its first derivative.


General Regression Neural Networks. (Python package).

Authors: Federico Amato

Python implementation of General Regression Neural Network (GRNN, also known as Nadaraya-Watson Estimator). A Feature Selection module based on GRNN is also provided.


MultiFractal Detrended Fluctuation Analysis. (R package).

Authors: Mohamed Laib, Luciano Telesca, Mikhail Kanevski

Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.


Intrinsic Dimension for Data Mining. (R package).

Authors: Jean Golay and Mohamed Laib

Contains techniques for mining large and high-dimensional data sets by using the concept of Intrinsic Dimension (ID). Here the ID is not necessarily an integer. It is extended to fractal dimensions. And the Morisita estimator is used for the ID estimation, but other tools are included as well.


Space Filling Based Tools for Data Mining. (R package).

Contains space filling based tools for machine learning and data mining. Some functions offer several computational techniques and deal with the out of memory for large big data by using the ff package.

Authors: Mohamed Laib and Mikhail Kanevski



Professor Kanevski also authored Geostat Office and Machine Learning Office, available with his popular books “Analysis and modelling of spatial environmental data” and “Machine Learning for Spatial Environmental Data“, respectively.