"The book targets professionals and graduate students in physics, chemistry, biology, computer science, information theory, economics, environmental science and others, as an introduction to Random Processes Analysis (RPA) using R computer language. It seeks to put RPA within the operational reach of the projected audience, and provides readers with hands-on practical experience in applying RPA with simple numerical examples and applications taken from the targeted disciplines. The book is organized around a practical framework for soundly applying RPA in empirical work. First, consistent with modern trends in university instruction, the book make readers active learners. Second, the book provides readers with an explicit framework-condensed from sound empirical practices recommended in the literature-that details a step-by-step procedure for applying RPA in real-world data application of RPA concepts. Therefore, this book is intended to present concepts, theory and computer code written in R, that helps readers with limited initial knowledge of RPA to become operational with the material. Each subject is described and problems are implemented in the R code, with real data collected in experiments performed by the authors or taken from the literature. Various subjects are described such as a Poisson processes, Markov chains, Random walk, Spectrum Analysis, Montecarlo, Bayesian inference, Genetic Algorithms and Spatial Analysis.

The book ends with a chapter addressing randomness from a mathematical and philosophical standpoint"--.