About the authors:

This project was born as a collaboration between two Machine Learning PhD students in the Intelligent Sistems and Data Mining Group at the University of Castilla-La Mancha in Albacete , Spain.

It started as a simple idea trying to make a compilation of a bunch of useful R scripts that were develop to agilize the experiment analysis of their publications. Later on, they decided to share the idea with the rest of the research group components and motivated for the good reception they began the developing of this R package, to be shared with the community.

Personal Photo of Jacinto Arias

Jacinto Arias


Jacinto Arias is a PhD student in the field of Machine Learning and Data Mining, enthusiast of everything related with Data Science focuses his research in the field of Big Data and scalable algorithms studying new programming paradigms applied to the Bayesian networks formalisms. He likes to be organized and enjoys accomplishing it by using R. He is a supporter of free software and emergent scientific programming stacks. recombines his programming hobbies with being an amateur musician.

Javier Cozar


Javier Cózar is a PhD student in the area of Computer Science. Nowadays his field of study is Machine Learning and Data Mining, focusing on Fuzzy Logic Theory and the design of learning algorithms for Fuzzy Rule Based Systems models. His lastly discovered passion is to analyze data and results with R, improving the quality of his works immensely. His hobbies range from doing sports to playing musical instruments.

Personal Photo of Javier Cozar

SIMD - Intelligent Systems and Data Mining Group

SIMD logo

The Intelligent Systems and Data Mining ( SIMD ) research group was born in 2000 under the direction of José Antonio Gámez Martín and José Miguel Puerta Callejón, and is part of the Computer Systems Department of the University of Castilla-La Mancha. Their main research is focused on:

  • Data Mining and Machine Learning: Knowledge Discovery and its applications as Bayesian networks, decision trees and rule-based systems.
  • Decision Making systems under conditions of uncertainty: probabilistic expert systems ( Bayesian networks ), fuzzy rule-based systems
  • Metaheuristics and evolutionary computation: genetic algorithms, ant colonies, distribution estimation algorithms, and its application in combinatorial optimization and search problems.
  • Mobile robotics: localization algorithms for mobile robots and image processing for object recognition
  • Multimodal interaction: multimodal communication mechanisms for social robots.