The detection of change-points, as a recurrent problem in the time series and signal processing literature, has given rise to a wide variety of methodologies over the years. [2, 27] have taken the first steps toward standardizing this problem by defining it in terms of components that are common across almost every solution. However, at a software level, there are still a gap in R [21] that implements this conceptual framework in a practical manner. In this context, tidychange-point [6] is introduced as a unifying actor for the implementation of diverse change-point detection methods. Using this package, change-points were detected across four simulated datasets, revealing consistent strengths across all applied methods and reinforcing the proposals of [15, 24, 25] regarding the use of approximate and metaheuristic methodologies when uncertainty exists about the underlying nature of the data.