Probabilistic programming is an emerging paradigm enabling software developers to model uncertainty of real data and to support suitable inference operations directly into computer programs. Probabilistic programs find applications in security/privacy, randomized algorithms, data prediction, generative and probabilistic machine learning and in modeling stochastic dynamical systems. A very challenging task is a how to automatically analyze statically the behavior of probabilistic programs with loops that can generate a continuous and infinite-state space. We provide an overview of the main results of ProbInG, an interdisciplinary project between computer science and statistics that aim to develop novel techniques for automatically analysing the behavior of program random variables in probabilistic loops.