I wrote the following R Code for an Integrated Trend Analysis (ITA) during my PhD thesis in 2010, when I attended for the first time the annual meeting of the ICES/HELCOM Working Group on Integrated Assessments of the Baltic Sea (WGIAB). The code helped running a cross-comparison of several Baltic Sea sub-systems (see the 2010 report1). Together with Rabea Diekmann we fine-tuned the code and published it along with a full description on ITA methods in a Book chapter2 in Climate Impacts on the Baltic Sea: From Science to Policy.
This post compares a few change point detection method available in R given different time series dynamics and research questions. Change points or breakpoints are abrupt variations in time series data and may represent transitions between different states. The detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, speech and image analysis or climate change detection.