 Approximate entropy, APAN, and sample entropy, SAMPAN, are two algorithms for measuring the regularity of time series data. Both algorithms are based on the idea of chaotic behavior, which can be described mathematically using concepts from information theory. APAN and SAMPAN measure the amount of randomness present in a given time series, and thus can be used to determine whether a system is chaotic or not. These algorithms have been applied in many fields including medicine, telecommunications, economics, and earth science. This paper provides a detailed explanation of the underlying theory, as well as practical examples of how to compute the algorithms. It also includes source code for computing the algorithms, making it easy for readers to apply them to their own data sets. This article was authored by Alfonso Delgado Bono and Alexander Marschak.