This chapter presents Transition Network Analysis (TNA) that captures the full breadth of the relational dynamics of a temporal process. TNA models transitions between events as a weighted directed network. In doing so, TNA brings the wealth of network analysis to the modeled process which include graph, node and edge level metrics. TNA also enables the detection of recurring patterns such as dyads or triads, and communities and clusters. More importantly, TNA allows researchers to statistically validate the findings using bootstrapping, permutation, and case dropping techniques to verify if and when the research conclusions are correct. Furthermore, TNA allows researchers to include covariates that would explain why certain patterns emerge or examine the differences across subgroups. Such statistical rigor that brings validation and hypothesis testing at each step of the analysis offers a method for researchers to build, verify and advance existing theories and develop new ones on the basis of a robust scientific approach. This chapter offers a step-by-step tutorial using the tna R package, illustrating all the TNA features in a case study about group regulation.